Data Sciense Course - Spring 1403
Project Phase1 - Introduction to Data Science
Mohammadreza Mohammadhashemi : 810100206
Soheil Hajian Manesh : 810100119
Mahdi Ebrahimi Soltani : 810100241
Problem Description¶
In this phase of our project, we will be focusing on web scraping data from the Fiverr.com website using BeautifulSoup and Selenium. The goal is to extract meaningful statistics from the dataset through exploratory data analysis (EDA), visualization, and preprocessing steps.Fiverr.com is an online marketplace that connects freelancers with clients who need various services. Launched in 2010, Fiverr offers a wide range of digital services, known as "gigs," which include categories such as graphic design, writing, translation, video editing, programming, digital marketing, and more. We collect about 6000 data records from 10 different categories.
In [ ]:
import pandas as pd
import numpy as np
from ydata_profiling import ProfileReport
from ydata_profiling.config import Settings
from ydata_profiling.report.presentation.flavours.html import templates
import matplotlib.pyplot as plt
import seaborn as sns
import gc
import scipy.stats as stats
In [ ]:
df = pd.read_csv("./FiverrFinal.csv")
In [ ]:
df.drop_duplicates(inplace=True)
In [ ]:
def print_unique_elements(df, columns):
for column in columns:
try:
print(f"\033[94m{column}\033[0m:")
unique_counts = df[column].value_counts()
i = 0
for value, count in unique_counts.items():
i = i + 1
print(f" \033[91m{value}\033[0m {count}")
except KeyError:
print(f" Column '{column}' not found in the DataFrame.")
In [ ]:
print_unique_elements(df, ['Basic Price'])
Basic Price: $10 678 $5 490 $100 412 $30 363 $15 327 $50 312 $20 301 $25 237 $150 189 $40 165 $35 146 $80 116 $200 102 $45 96 $90 95 $60 95 $75 73 $250 63 $120 61 €9.67 58 $300 57 $70 56 $125 56 $65 45 €4.84 44 $500 40 $95 36 $55 36 $140 35 €14.51 33 €19.34 29 $85 28 €48.36 27 €96.72 26 $350 26 $130 25 $110 24 $105 22 $160 19 €145.07 19 $180 18 $135 18 €29.01 18 $400 17 €24.18 17 $195 17 $145 15 $175 14 $1,000 14 $600 13 $115 12 $170 12 €87.04 11 $225 10 €43.52 10 $295 10 $190 9 $2,000 9 $450 9 ₪115.23 8 $220 8 €38.69 8 €193.43 7 €58.03 7 $245 7 €241.79 7 US$10 7 ₪19.21 7 £82.38 7 $50Save up to 10% with Subscribe to Save 6 $275 6 $375 6 US$30 6 €290.15 6 $800 6 £24.71 6 US$15 6 £4.12 6 US$50 6 €77.41 5 ₪192.06 5 $210 5 $185 5 $155 5 US$5 5 $650 5 $1,800 5 $1,200 5 $290 5 $1,500 5 $3,000 5 $995 5 £41.19 5 $240 5 ₪38.41 5 $20Save up to 10% with Subscribe to Save 4 €53.19 4 $270 4 $100Save up to 10% with Subscribe to Save 4 $900 4 $30Save up to 10% with Subscribe to Save 4 £123.57 4 $550 4 $10Save up to 10% with Subscribe to Save 4 €72.54 4 $4,000 4 $395 4 €33.85 4 €82.21 4 $475 4 $950 4 $165 4 €174.09 4 $325 4 $390 4 US$100 4 €106.39 3 ₪57.62 3 €116.11 3 $2,500 3 €96.76 3 $495 3 €154.74 3 ₪384.11 3 $750 3 $335 3 $590 3 ₪307.29 3 ₪96.03 3 £28.83 3 €67.70 3 €483.58 3 US$20 3 €125.73 3 $1,350 3 US$55 3 ₪576.17 3 ₪768.22 3 $255 3 €116.06 3 $45Save up to 10% with Subscribe to Save 2 £74.14 2 €82.25 2 $250Save up to 15% with Subscribe to Save 2 ₪960.28 2 £8.24 2 $340 2 $25Save up to 20% with Subscribe to Save 2 $40Save up to 5% with Subscribe to Save 2 $15Save up to 10% with Subscribe to Save 2 €435.22 2 £45.31 2 ₪134.44 2 $3,400 2 $380 2 $445 2 $25Save up to 10% with Subscribe to Save 2 US$150 2 US$70 2 US$35 2 $200Save up to 10% with Subscribe to Save 2 $230 2 ₪76.82 2 €338.50 2 $1,100 2 $1,495 2 $60Save up to 10% with Subscribe to Save 2 €77.37 2 $1,400 2 $1,250 2 $10Save up to 15% with Subscribe to Save 2 €91.88 2 €193.52 2 $700 2 $125Save up to 15% with Subscribe to Save 2 $480 2 $125Save up to 10% with Subscribe to Save 2 US$95 2 €19.35 2 ₪153.64 2 $30Save up to 5% with Subscribe to Save 2 $320 2 $285 2 $20,000 2 $490 2 US$155 1 ₪2,843 1 $1,875 1 $555 1 $740 1 €377.19 1 US$500 1 €149.91 1 ₪633.78 1 €285.31 1 $10,000 1 $405 1 €391.70 1 US$390 1 $280 1 $560 1 $440 1 $810 1 $7,500 1 €411.04 1 €531.93 1 US$1,200 1 US$200 1 $2,700 1 $95Save up to 5% with Subscribe to Save 1 €822.08 1 $4,500 1 €406.20 1 $505 1 $25Save up to 15% with Subscribe to Save 1 $540 1 US$120 1 $1,365 1 $455 1 €101.55 1 $795 1 $75Save up to 10% with Subscribe to Save 1 $460 1 €169.25 1 $2,695 1 $265 1 $1,600 1 US$10,000 1 US$25 1 $370 1 $250Save up to 20% with Subscribe to Save 1 $3,750 1 $890 1 €87.08 1 £12.36 1 $1,005 1 $1,225 1 €962.76 1 €382.20 1 €1,161 1 €967.60 1 US$35Save up to 10% with Subscribe to Save 1 $430 1 $360 1 £2,472 1 $660 1 €488.64 1 €125.79 1 $315 1 $1,295 1 $6,500 1 $2,900 1 $3,600 1 €130.57 1 $610 1 $50Save up to 5% with Subscribe to Save 1 $745 1 £20.60 1 $15Save up to 20% with Subscribe to Save 1 €29.01Save up to 10% with Subscribe to Save 1 US$190 1 $60Save up to 15% with Subscribe to Save 1 €48.36Save up to 15% with Subscribe to Save 1 €164.42Save up to 10% with Subscribe to Save 1 US$65 1 $170Save up to 10% with Subscribe to Save 1 ₪518.55 1 €9.67Save up to 20% with Subscribe to Save 1 US$45 1 US$350 1 ₪172.85 1 €507.76 1 €48.36Save up to 5% with Subscribe to Save 1 $670 1 $200Save up to 20% with Subscribe to Save 1 €241.90 1 $150Save up to 10% with Subscribe to Save 1 €628.65Save up to 10% with Subscribe to Save 1 $690 1 $100Save up to 15% with Subscribe to Save 1 $15Save up to 5% with Subscribe to Save 1 €774.08 1 $980 1 £152.41 1 $20Save up to 20% with Subscribe to Save 1 $525 1 €435.42 1 $50Save up to 20% with Subscribe to Save 1 $20Save up to 15% with Subscribe to Save 1 $80Save up to 20% with Subscribe to Save 1 €33.87 1 €9.68 1 £107.10Save up to 10% with Subscribe to Save 1 $30Save up to 20% with Subscribe to Save 1 £1,607Save up to 20% with Subscribe to Save 1 ₪364.91 1 $425 1 ₪2,497 1 £131.81 1 €967.15 1 €333.67Save up to 15% with Subscribe to Save 1 €478.74 1 ₪921.87 1 $11,400 1 $3,200 1 €203.10 1 $3,500 1 $1,300 1 €3,676 1 $20Save up to 5% with Subscribe to Save 1 ₪749.02 1 $1,755 1 $920 1 €449.73 1 $10Save up to 5% with Subscribe to Save 1 €120.89 1 $35Save up to 20% with Subscribe to Save 1 €962.32 1 £61.79 1 $15Save up to 15% with Subscribe to Save 1 $100Save up to 20% with Subscribe to Save 1 US$300 1 $60Save up to 20% with Subscribe to Save 1 $120Save up to 20% with Subscribe to Save 1 $345 1 ₪249.67 1 £65.90 1 US$110 1 $235 1 US$290 1 US$295 1 $3,800 1 $990 1
In [ ]:
df['Seller In Same Level'] = df['Seller In Same Level'].str.replace(',', '').str.replace('+', '').str.extract('(\d+)').astype(int)
In [ ]:
print_unique_elements(df, ['Seller In Same Level'])
Seller In Same Level: 1000 226 2800 183 2000 133 313 130 8900 96 2200 95 1800 94 2700 94 12000 93 606 92 1100 91 2400 91 533 89 1200 89 23 68 683 48 6100 48 44000 48 4300 48 298 48 26000 48 5900 48 3800 48 53000 48 33000 48 1773 48 6000 48 1700 48 7281 48 16000 48 2100 48 2900 48 50472 48 361 48 2248 48 5500 48 15000 48 641 47 620 47 7400 47 14000 47 1600 47 1500 47 10000 47 120000 47 400 47 4600 47 2600 46 959 46 17000 46 6176 46 895 46 6393 46 524 46 662 46 23000 46 449 46 553 46 41000 46 6400 46 75000 46 2500 46 258 45 988 45 8100 45 19000 45 763 45 5800 45 7800 45 960 44 352 44 177 44 502 44 155 44 170 44 198 44 496 44 550 44 608 43 107 43 215 43 401 43 328 43 406 43 173 43 329 43 459 43 151 42 315 42 128 42 7900 42 100 42 5400 42 126 42 181 41 76 41 292 41 293 40 132 39 92 38 35 38 172 38 38 38 461 37 55 35 34 34 65 34 32 32 27 27 25 26 22 24 18 20 9 9 1730 8 7 7 2 2 1 1
In [ ]:
df['Basic Price'] = df['Basic Price'].str.split("Save up to").str[0]
print_unique_elements(df, ['Basic Price'])
Basic Price: $10 685 $5 490 $100 418 $30 370 $15 332 $50 320 $20 308 $25 242 $150 190 $40 167 $35 147 $80 117 $200 105 $60 99 $45 98 $90 95 $75 74 $250 66 $120 62 $125 60 €9.67 59 $300 57 $70 56 $65 45 €4.84 44 $500 40 $95 37 $55 36 $140 35 €14.51 33 €48.36 29 €19.34 29 $85 28 €96.72 26 $350 26 $130 25 $110 24 $105 22 €145.07 19 $160 19 €29.01 19 $180 18 $135 18 $400 17 $195 17 €24.18 17 $145 15 $175 14 $1,000 14 $600 13 $170 13 $115 12 €87.04 11 $225 10 €43.52 10 $295 10 $450 9 $190 9 $2,000 9 $220 8 ₪115.23 8 €38.69 8 ₪19.21 7 €58.03 7 $245 7 €241.79 7 €193.43 7 £82.38 7 US$10 7 $375 6 $800 6 £24.71 6 €290.15 6 US$50 6 US$30 6 £4.12 6 $275 6 US$15 6 ₪192.06 5 $3,000 5 ₪38.41 5 $290 5 $995 5 $240 5 US$5 5 $210 5 $155 5 $1,200 5 $1,800 5 $650 5 £41.19 5 $185 5 $1,500 5 €77.41 5 $550 4 $165 4 $390 4 $325 4 US$100 4 €174.09 4 $4,000 4 $475 4 €72.54 4 €82.21 4 $395 4 $270 4 $900 4 €53.19 4 €33.85 4 $950 4 £123.57 4 US$20 3 ₪307.29 3 €116.11 3 €67.70 3 $590 3 ₪96.03 3 ₪57.62 3 £28.83 3 US$35 3 €483.58 3 €106.39 3 US$55 3 ₪768.22 3 €154.74 3 $495 3 $750 3 $1,350 3 $2,500 3 $255 3 $335 3 €125.73 3 €96.76 3 ₪384.11 3 ₪576.17 3 €116.06 3 €82.25 2 $1,100 2 £45.31 2 ₪153.64 2 ₪134.44 2 $380 2 €435.22 2 $490 2 €91.88 2 $1,250 2 US$150 2 US$70 2 $700 2 US$95 2 $230 2 $1,495 2 ₪960.28 2 $340 2 $3,400 2 $320 2 ₪76.82 2 €193.52 2 €77.37 2 $480 2 $445 2 £74.14 2 $20,000 2 $1,400 2 €19.35 2 €338.50 2 $285 2 £8.24 2 ₪749.02 1 $7,500 1 €967.60 1 €382.20 1 €962.76 1 $2,700 1 $1,225 1 $1,005 1 US$155 1 £12.36 1 $890 1 €822.08 1 €967.15 1 US$200 1 $555 1 $1,875 1 $10,000 1 €377.19 1 US$500 1 €149.91 1 $280 1 $440 1 US$1,200 1 US$390 1 $560 1 €3,676 1 €507.76 1 €87.08 1 £2,472 1 $370 1 US$25 1 US$10,000 1 $1,600 1 $265 1 $2,695 1 €169.25 1 $460 1 $430 1 $795 1 $4,500 1 €101.55 1 $455 1 $1,365 1 US$120 1 $540 1 $505 1 €406.20 1 $3,750 1 $360 1 $2,900 1 $6,500 1 $1,295 1 $315 1 €125.79 1 $405 1 $660 1 €488.64 1 €411.04 1 $810 1 $690 1 £131.81 1 ₪2,497 1 $425 1 $920 1 $980 1 €449.73 1 $345 1 €241.90 1 £152.41 1 US$295 1 $525 1 €435.42 1 $235 1 US$110 1 £65.90 1 ₪249.67 1 €120.89 1 €33.87 1 €9.68 1 £61.79 1 $670 1 US$65 1 $745 1 £20.60 1 US$190 1 ₪518.55 1 US$45 1 €774.08 1 €130.57 1 $3,200 1 €478.74 1 €531.93 1 ₪172.85 1 $3,800 1 €391.70 1 ₪2,843 1 €285.31 1 ₪633.78 1 US$290 1 $740 1 $3,600 1 €1,161 1 $610 1 £1,607 1 $1,755 1 US$300 1 €962.32 1 £107.10 1 $1,300 1 US$350 1 €164.42 1 $3,500 1 €203.10 1 $11,400 1 €333.67 1 ₪364.91 1 ₪921.87 1 €628.65 1 $990 1
In [ ]:
from forex_python.converter import CurrencyRates
def find_exchange_rates():
try:
cr = CurrencyRates()
euro_ex_rate = cr.get_rate('EUR', 'USD')
shekle_ex_rate = cr.get_rate('ILS', 'USD')
pound_ex_rate = cr.get_rate('GBP', 'USD')
return {'$': 1, '€': euro_ex_rate, '₪': shekle_ex_rate, 'US$': 1, '£': pound_ex_rate}
except :
return {'$': 1, '€': 1.08, '₪': 0.27, 'US$': 1, '£': 1.27}
In [ ]:
exchange_rates = find_exchange_rates()
def convert_to_usd(price):
for currency, exchange_rate in exchange_rates.items():
if price.startswith(currency):
numeric_price = float(price.lstrip(currency).replace(',', ''))
return round(numeric_price / exchange_rate, 2)
return price
df['Basic Price'] = df['Basic Price'].apply(convert_to_usd)
In [ ]:
print_unique_elements(df, ['Basic Price'])
Basic Price: 10.0 692 5.0 495 100.0 422 30.0 376 15.0 338 50.0 326 20.0 311 25.0 243 150.0 192 40.0 167 35.0 150 80.0 117 200.0 106 60.0 99 45.0 99 90.0 95 75.0 74 250.0 66 120.0 63 125.0 60 8.95 59 300.0 58 70.0 58 65.0 46 4.48 44 500.0 41 55.0 39 95.0 39 140.0 35 13.44 33 44.78 29 17.91 29 85.0 28 350.0 27 89.56 26 110.0 25 130.0 25 105.0 22 134.32 19 26.86 19 160.0 19 180.0 18 135.0 18 195.0 17 22.39 17 400.0 17 145.0 15 175.0 14 1000.0 14 600.0 13 170.0 13 115.0 12 80.59 11 295.0 11 190.0 10 225.0 10 40.3 10 450.0 9 2000.0 9 35.82 8 426.78 8 220.0 8 71.15 7 53.73 7 64.87 7 245.0 7 223.88 7 179.1 7 800.0 6 290.0 6 268.66 6 1200.0 6 3.24 6 375.0 6 275.0 6 155.0 6 19.46 6 210.0 5 3000.0 5 995.0 5 650.0 5 1500.0 5 71.68 5 142.26 5 1800.0 5 32.43 5 390.0 5 711.33 5 185.0 5 240.0 5 67.17 4 76.12 4 97.3 4 475.0 4 900.0 4 270.0 4 49.25 4 325.0 4 161.19 4 31.34 4 4000.0 4 950.0 4 165.0 4 550.0 4 395.0 4 2500.0 3 1422.63 3 89.59 3 750.0 3 335.0 3 1138.11 3 355.67 3 590.0 3 22.7 3 143.28 3 62.69 3 213.41 3 98.51 3 447.76 3 116.42 3 255.0 3 1350.0 3 2845.26 3 107.51 3 107.46 3 2133.96 3 495.0 3 445.0 2 3400.0 2 380.0 2 490.0 2 76.16 2 58.38 2 1495.0 2 284.52 2 20000.0 2 569.04 2 313.43 2 700.0 2 1100.0 2 1250.0 2 402.98 2 85.07 2 10000.0 2 230.0 2 71.64 2 6.49 2 1400.0 2 340.0 2 35.68 2 17.92 2 480.0 2 179.19 2 285.0 2 3556.59 2 497.93 2 320.0 2 349.25 1 2695.0 1 761.19 1 555.0 1 890.0 1 9.73 1 265.0 1 1365.0 1 1600.0 1 138.81 1 280.0 1 1005.0 1 370.0 1 560.0 1 1875.0 1 2700.0 1 7500.0 1 405.0 1 440.0 1 1225.0 1 156.71 1 452.44 1 505.0 1 376.11 1 3750.0 1 360.0 1 2900.0 1 6500.0 1 1295.0 1 315.0 1 116.47 1 455.0 1 660.0 1 540.0 1 1946.46 1 430.0 1 94.03 1 80.63 1 4500.0 1 795.0 1 895.93 1 460.0 1 353.89 1 891.44 1 670.0 1 610.0 1 810.0 1 492.53 1 345.0 1 3800.0 1 891.04 1 235.0 1 84.33 1 51.89 1 924.7 1 111.94 1 152.24 1 48.65 1 308.95 1 582.08 1 690.0 1 716.74 1 980.0 1 470.15 1 640.19 1 223.98 1 120.01 1 1920.56 1 525.0 1 403.17 1 31.36 1 8.96 1 16.22 1 416.42 1 3200.0 1 920.0 1 1755.0 1 380.59 1 2774.15 1 362.69 1 10529.63 1 264.18 1 2347.33 1 740.0 1 3600.0 1 1075.0 1 745.0 1 1265.35 1 3403.7 1 425.0 1 1300.0 1 3500.0 1 188.06 1 11400.0 1 1351.52 1 3414.33 1 443.28 1 120.9 1 895.51 1 103.79 1 9248.15 1 990.0 1
In [ ]:
df['Standard Price'] = df['Standard Price'].str.split("Save up to").str[0]
df['Standard Price'] = df['Standard Price'].apply(convert_to_usd)
print_unique_elements(df, ['Standard Price'])
Standard Price: 50.0 356 20.0 263 100.0 243 30.0 235 10.0 233 150.0 227 25.0 226 40.0 202 15.0 195 200.0 182 60.0 178 250.0 159 80.0 148 70.0 137 300.0 116 120.0 108 35.0 107 75.0 105 90.0 102 45.0 93 5.0 79 500.0 76 350.0 69 400.0 60 180.0 60 65.0 58 95.0 53 55.0 52 125.0 52 130.0 41 160.0 41 600.0 37 140.0 36 450.0 36 110.0 36 175.0 32 1000.0 32 85.0 32 195.0 31 44.78 30 1500.0 27 190.0 26 225.0 25 550.0 25 8.95 23 155.0 23 17.91 22 22.39 20 220.0 20 800.0 20 13.44 20 170.0 20 280.0 19 115.0 18 26.86 18 240.0 17 295.0 17 270.0 16 165.0 15 145.0 15 135.0 15 750.0 14 179.1 14 230.0 14 320.0 14 134.32 14 105.0 14 1200.0 13 700.0 13 4000.0 13 89.56 13 2000.0 12 395.0 12 2500.0 12 290.0 11 5000.0 11 67.17 11 650.0 11 260.0 11 71.64 11 275.0 11 223.88 11 210.0 11 35.82 10 185.0 10 245.0 10 375.0 10 53.73 10 268.66 9 3000.0 9 31.34 9 62.69 9 360.0 8 107.46 8 495.0 8 4.48 8 325.0 8 595.0 8 40.3 7 205.0 7 447.76 7 1800.0 7 900.0 7 475.0 6 215.0 6 358.2 6 582.08 5 480.0 5 285.0 5 390.0 5 58.2 5 853.59 5 161.19 5 2400.0 4 420.0 4 850.0 4 330.0 4 995.0 4 695.0 4 71.15 4 235.0 4 380.0 4 1995.0 4 626.86 4 355.67 4 1250.0 4 80.59 4 116.42 4 1422.63 4 64.87 4 711.33 4 537.31 4 98.51 3 675.0 3 156.71 3 590.0 3 174.62 3 990.0 3 490.0 3 305.0 3 340.0 3 4500.0 3 38.92 3 129.73 3 49.25 3 6000.0 3 255.0 3 9.73 3 1707.15 3 250.74 3 310.0 3 540.0 3 365.0 3 560.0 3 4270.37 3 1495.0 3 1100.0 3 1375.0 3 795.0 3 497.93 2 1066.96 2 143.28 2 89.59 2 430.0 2 197.01 2 7000.0 2 111.94 2 2800.0 2 129.85 2 569.04 2 32.43 2 402.98 2 345.0 2 1600.0 2 268.78 2 530.0 2 206.06 2 335.81 2 492.53 2 2845.26 2 630.0 2 1450.0 2 525.0 2 1300.0 2 1050.0 2 445.0 2 201.49 2 2560.74 2 162.17 2 29.19 2 3995.0 2 1795.0 2 315.0 2 97.3 2 250.86 2 123.24 2 10000.0 2 8000.0 2 85.07 2 3800.0 2 1350.0 2 625.0 2 510.0 2 1700.0 2 313.57 2 113.52 2 308.95 2 3500.0 2 161.27 2 2100.0 2 284.52 2 76.12 2 4477.78 2 20000.0 2 213.41 2 6.49 2 1343.52 2 640.0 2 165.75 1 850.74 1 2990.0 1 1210.0 1 179.19 1 1792.59 1 1280.0 1 194.6 1 1490.0 1 3985.19 1 1810.0 1 286.56 1 2995.0 1 4100.0 1 15000.0 1 2235.19 1 1400.0 1 16.22 1 761.19 1 820.0 1 1410.0 1 7500.0 1 1710.0 1 2700.0 1 5400.0 1 4725.0 1 4550.0 1 1787.96 1 658.51 1 745.0 1 376.11 1 12.98 1 1791.67 1 147.76 1 1075.0 1 116.76 1 2310.0 1 895.0 1 950.0 1 165.67 1 1520.0 1 90.81 1 142.26 1 265.0 1 1590.0 1 370.0 1 1778.3 1 995.85 1 2185.0 1 485.0 1 1820.0 1 780.0 1 760.0 1 2250.0 1 273.13 1 1950.0 1 1485.0 1 3892.13 1 443.48 1 188.15 1 71.35 1 286.69 1 685.0 1 631.63 1 246.38 1 1565.0 1 2670.0 1 9750.0 1 710.0 1 12000.0 1 5900.0 1 575.0 1 479.1 1 1330.0 1 232.83 1 3295.0 1 443.28 1 48.65 1 665.0 1 2489.59 1 26.88 1 44.8 1 8.96 1 71.68 1 17.92 1 870.0 1 565.0 1 335.0 1 410.0 1 880.0 1 2062.81 1 694.02 1 313.43 1 19.46 1 58.38 1 74.6 1 42.17 1 790.0 1 1138.11 1 81.09 1 545.0 1 3.24 1 627.15 1 120.01 1 5800.0 1 3556.59 1 865.0 1 528.35 1 455.0 1 6500.0 1 2774.15 1 7114.81 1 4657.41 1 5600.0 1 4400.0 1 35.83 1 7400.0 1 2300.0 1 891.04 1 640.19 1 214.93 1 84.33 1 152.24 1 690.0 1 1344.44 1 125.37 1 205.97 1 945.0 1 895.51 1 716.41 1 3900.0 1 1505.0 1 5692.59 1 2505.0 1 1265.35 1 1215.0 1 2238.89 1 840.0 1 1095.0 1 1338.89 1 5977.78 1 622.38 1 425.0 1 15581.48 1 324.34 1 599.99 1 2895.0 1 940.74 1 740.0 1 520.0 1 405.0 1 4050.0 1 295.52 1 1900.0 1 920.0 1 825.0 1 18496.3 1 2150.0 1 440.0 1 142.71 1 331.34 1 385.0 1 183.58 1 925.0 1 11400.0 1 10670.37 1 1493.78 1 645.0 1 711.94 1 355.0 1 1875.0 1 4981.48 1 1636.04 1 415.0 1 1388.89 1
In [ ]:
df['Premium Price'] = df['Premium Price'].str.split("Save up to").str[0]
df['Premium Price'] = df['Premium Price'].apply(convert_to_usd)
print_unique_elements(df, ['Premium Price'])
Premium Price: 100.0 365 50.0 237 200.0 220 150.0 217 30.0 167 300.0 156 250.0 150 500.0 143 60.0 140 20.0 138 40.0 135 120.0 122 25.0 119 15.0 113 80.0 111 400.0 110 90.0 108 350.0 102 45.0 91 5.0 79 35.0 78 75.0 72 10.0 71 450.0 68 70.0 66 180.0 66 1000.0 66 65.0 56 600.0 55 95.0 54 130.0 53 125.0 53 55.0 46 160.0 46 85.0 45 110.0 43 1500.0 39 175.0 34 800.0 32 700.0 31 170.0 30 190.0 29 750.0 29 195.0 28 220.0 25 240.0 25 1200.0 24 650.0 24 89.56 24 225.0 23 2000.0 22 140.0 22 550.0 22 3000.0 21 135.0 20 44.78 20 145.0 20 495.0 20 2500.0 18 320.0 18 115.0 18 375.0 18 185.0 18 230.0 18 900.0 18 995.0 17 179.1 17 105.0 16 270.0 16 10000.0 16 295.0 15 210.0 15 390.0 15 395.0 15 134.32 15 26.86 14 280.0 14 290.0 14 155.0 14 17.91 13 35.82 13 850.0 13 5000.0 13 260.0 13 950.0 13 40.3 12 8000.0 12 268.66 12 165.0 12 223.88 12 71.64 11 22.39 11 360.0 11 480.0 11 1250.0 10 53.73 10 325.0 10 13.44 10 275.0 10 8.95 9 4000.0 9 3500.0 8 595.0 8 345.0 8 590.0 8 1600.0 8 990.0 8 4.48 8 385.0 8 1100.0 8 490.0 8 4500.0 8 447.76 8 358.2 7 475.0 7 255.0 7 125.37 7 420.0 7 520.0 7 895.51 6 116.42 6 425.0 6 460.0 6 67.17 6 285.0 6 6000.0 6 370.0 6 7500.0 6 20000.0 6 402.98 6 1800.0 6 245.0 6 380.0 6 80.59 6 107.46 5 340.0 5 540.0 5 330.0 5 313.43 5 111.94 5 795.0 5 1138.11 5 1350.0 5 625.0 4 76.12 4 695.0 4 129.73 4 4981.48 4 97.3 4 315.0 4 71.15 4 305.0 4 2133.96 4 215.0 4 440.0 4 335.0 4 980.0 4 530.0 4 161.19 4 537.31 4 430.0 4 1343.52 4 205.0 4 497.93 3 445.0 3 7000.0 3 975.0 3 12.98 3 365.0 3 58.2 3 525.0 3 415.0 3 4495.0 3 31.34 3 15000.0 3 49.25 3 2800.0 3 197.01 3 1300.0 3 2100.0 3 2700.0 3 891.04 3 775.0 3 355.0 3 1422.63 3 492.53 3 690.0 3 152.24 3 1150.0 3 605.0 3 671.64 3 1400.0 3 711.33 3 675.0 3 310.0 3 143.28 2 725.0 2 660.0 2 630.0 2 680.0 2 213.41 2 2200.0 2 45.41 2 61.62 2 570.0 2 265.0 2 286.56 2 1209.22 2 8955.56 2 2238.89 2 353.73 2 58.38 2 470.0 2 1254.63 2 7800.0 2 1900.0 2 1450.0 2 1995.0 2 2687.04 2 925.0 2 1040.0 2 324.34 2 886.56 2 2995.0 2 2490.0 2 3200.0 2 4995.0 2 98.51 2 6500.0 2 455.0 2 14229.63 2 313.57 2 880.0 2 2400.0 2 3135.19 2 1791.67 2 780.0 2 35566.67 2 1195.0 2 3600.0 2 3556.59 2 194.6 2 670.0 2 3200.93 2 405.0 2 1850.0 2 1795.0 2 62.69 2 2300.0 2 667.16 2 740.0 2 181.63 2 162.17 2 720.0 2 403.17 2 235.0 2 582.08 2 985.19 2 219.4 2 585.0 2 29.19 1 5407.41 1 761.19 1 8500.0 1 1120.0 1 7020.0 1 438.81 1 4125.0 1 6215.0 1 745.0 1 545.0 1 9985.0 1 9100.0 1 8059.26 1 945.37 1 7167.59 1 1260.0 1 344.94 1 1495.0 1 250.86 1 8900.0 1 7495.0 1 3750.0 1 3310.0 1 850.74 1 3990.0 1 3250.0 1 6300.0 1 845.0 1 2720.0 1 2821.3 1 1105.0 1 2160.0 1 518.94 1 716.41 1 5990.0 1 2642.59 1 3050.0 1 4270.37 1 3400.0 1 9995.0 1 2020.0 1 10500.0 1 2975.0 1 13500.0 1 439.0 1 7995.0 1 465.67 1 430.05 1 259.81 1 165.67 1 85.07 1 4585.0 1 4900.0 1 505.0 1 4200.0 1 1095.0 1 1330.56 1 1790.0 1 429.84 1 2270.0 1 655.0 1 2010.0 1 407.45 1 4545.0 1 2850.0 1 201.49 1 575.0 1 1030.56 1 5030.0 1 1220.0 1 1750.0 1 1351.52 1 322.39 1 3020.0 1 227.03 1 1905.0 1 2195.0 1 1050.0 1 891.44 1 3100.0 1 376.29 1 3695.0 1 129.85 1 156.71 1 9730.71 1 1385.0 1 820.0 1 179.19 1 134.39 1 895.93 1 309.09 1 188.06 1 3675.0 1 645.0 1 447.96 1 985.0 1 721.22 1 358.37 1 1925.0 1 1275.0 1 4090.0 1 12000.0 1 3350.0 1 8795.0 1 7505.0 1 790.0 1 945.0 1 16.22 1 77.84 1 10800.0 1 6800.0 1 259.69 1 5622.22 1 138.81 1 3272.07 1 214.93 1 640.19 1 113.52 1 19.46 1 64.87 1 183.58 1 188.12 1 6985.19 1 241.79 1 13659.26 1 940.0 1 995.85 1 2774.15 1 1290.0 1 452.23 1 1564.89 1 250.74 1 174.62 1 1636.04 1 4555.56 1 2750.0 1 1345.0 1 420.89 1 895.0 1 22.4 1 3.24 1 627.15 1 35.83 1 71.68 1 120.01 1 152.31 1 85.11 1 17.92 1 2805.0 1 970.0 1 5745.0 1 2419.44 1 48.65 1 308.95 1 51.89 1 84.33 1 2560.74 1 782.44 1 145.95 1 6.49 1 1595.0 1 1305.0 1 284.52 1 355.67 1 10670.37 1 6403.7 1 1180.0 1 2820.0 1 1645.0 1 9600.0 1 1782.41 1 5500.0 1 3800.0 1 1090.0 1 1523.15 1 9000.0 1 805.96 1 3505.0 1 1265.35 1 3995.0 1 2220.0 1 3582.41 1 3195.0 1 5692.59 1 541.79 1 615.0 1 2060.0 1 3577.78 1 1338.89 1 29307.41 1 1297.64 1 841.78 1 4523.15 1 2284.26 1 5600.0 1 1650.0 1 810.0 1 465.0 1 168.65 1 805.0 1 317.91 1 1075.0 1 555.22 1 1550.0 1 411.94 1 228.35 1 860.0 1 920.0 1 760.0 1 1225.0 1 1280.0 1 1700.0 1 840.0 1 501.49 1 580.0 1 2295.0 1 394.03 1 191.35 1 1210.0 1 610.0 1 960.0 1 514.92 1 981.48 1 8537.04 1 2062.81 1 11400.0 1 483.57 1 2250.0 1
In [ ]:
print_unique_elements(df , ['Basic Delivery'])
Basic Delivery: 2 days 1305 3 days 1109 1 day 1105 5 days 442 7 days 440 4 days 322 10 days 182 14 days 152 2-day delivery 142 1-day delivery 130 3-day delivery 114 7-day delivery 81 6 days 66 30 days 64 21 days 56 5-day delivery 56 4-day delivery 39 10-day delivery 29 30-day delivery 28 14-day delivery 27 21-day delivery 13 6-day delivery 7 90-day delivery 3 45 days 3 60 days 2 45-day delivery 2 29-day delivery 1 60-day delivery 1 90 days 1
In [ ]:
df['Basic Delivery'] = df['Basic Delivery'].str.extract('(\d+)').astype(int)
print_unique_elements(df , ['Basic Delivery'])
Basic Delivery: 2 1447 1 1235 3 1223 7 521 5 498 4 361 10 211 14 179 30 92 6 73 21 69 45 5 90 4 60 3 29 1
In [ ]:
df['Standard Delivery'] = df['Standard Delivery'].str.extract('(\d+)').astype(int)
print_unique_elements(df , ['Standard Delivery'])
Standard Delivery: 3 1106 2 1006 5 777 7 709 4 613 1 453 10 381 14 324 6 184 30 161 21 158 45 24 60 17 90 4 75 2 15 2 29 1
In [ ]:
df['Premium Delivery'] = df['Premium Delivery'].str.extract('(\d+)').astype(int)
print_unique_elements(df , ['Premium Delivery'])
Premium Delivery: 7 966 3 829 5 742 10 589 2 532 4 498 14 457 1 353 30 352 6 231 21 218 45 63 60 43 90 35 75 9 8 2 29 1 28 1 20 1
In [ ]:
print_unique_elements(df , ['Basic Revision'])
Basic Revision: -1 2340 1 1060 Unlimited 791 2 657 0 323 3 322 5 99 1 Revision 87 Unlimited Revisions 71 2 Revisions 53 4 41 3 Revisions 30 9 16 5 Revisions 12 6 8 7 5 4 Revisions 3 8 2 8 Revisions 1 9 Revisions 1
Raplce Unlimited Revision with a very high value (100) to this field become numeric¶
In [ ]:
df['Basic Revision'] = df['Basic Revision'].replace('Unlimited Revisions', '100').str.extract('(-?\d+)', expand=False).fillna(0).astype(int)
print_unique_elements(df , ['Basic Revision'])
Basic Revision: -1 2340 1 1147 0 1114 2 710 3 352 5 111 100 71 4 44 9 17 6 8 7 5 8 3
In [ ]:
df['Standard Revision'] = df['Standard Revision'].replace('Unlimited Revisions', '100').str.extract('(-?\d+)', expand=False).fillna(0).astype(int)
print_unique_elements(df , ['Standard Revision'])
Standard Revision: -1 2340 0 1031 2 887 1 668 3 484 5 218 4 117 100 71 9 37 6 31 7 24 8 14
In [ ]:
df['Premium Revision'] = df['Premium Revision'].replace('Unlimited Revisions', '100').str.extract('(-?\d+)', expand=False).fillna(0).astype(int)
print_unique_elements(df , ['Premium Revision'])
Premium Revision: -1 2340 0 1281 2 631 3 583 1 456 5 254 4 136 100 71 9 66 7 42 6 37 8 25
In [ ]:
print_unique_elements(df, ['Rating Count'])
Rating Count: 3 104 2 102 1.0 101 1 93 4 85 5 80 7 76 11 72 12 71 3.0 70 2.0 67 13 66 10 66 21 65 16 63 15 63 6 62 22 58 20 53 17 52 18 51 14 50 24 47 19 47 25 46 26 46 8 44 9 44 33 43 5.0 41 35 40 29 39 30 39 23 36 28 34 31 34 27 33 40 33 4.0 33 34 32 15.0 30 8.0 29 9.0 29 39 28 6.0 27 11.0 27 48 26 37 25 44 25 12.0 25 41 25 63 24 7.0 24 43 24 79 24 45 24 46 24 32 24 47 23 18.0 23 36 23 49 23 53 23 38 23 42 22 56 21 10.0 20 62 20 51 20 50 20 60 19 17.0 19 66 18 64 18 54 17 16.0 17 57 17 91 17 61 17 73 16 70 16 52 16 101 16 93 15 59 15 14.0 15 75 15 92 15 81 15 25.0 15 20.0 14 55 14 23.0 14 68 14 82 14 84 14 131 13 99 13 89 12 13.0 12 153 12 107 12 76 12 78 12 24.0 12 35.0 11 108 11 80 11 71 11 110 11 77 11 122 11 94 11 96 11 114 11 36.0 10 145 10 58 10 97 10 19.0 10 87 10 197 10 65 10 115 10 72 10 67 10 86 10 177 10 124 9 143 9 219 9 83 9 175 9 29.0 9 123 9 102 9 21.0 9 103 9 74 9 198 9 22.0 9 34.0 9 144 9 162 9 104 8 182 8 106 8 135 8 185 8 293 8 174 8 45.0 8 69 8 150 8 138 8 105 8 118 8 201 8 100 8 139 8 109 8 211 7 98 7 112 7 209 7 111 7 38.0 7 195 7 300 7 116 7 85 7 192 7 128 7 247 7 141 7 137 7 31.0 7 164 7 78.0 7 199 7 218 7 28.0 7 56.0 6 171 6 183 6 129 6 229 6 27.0 6 58.0 6 147 6 243 6 134 6 163 6 412 6 307 6 193 6 41.0 6 26.0 6 231 6 155 6 237 6 217 6 203 6 309 6 187 6 287 6 119 5 148 5 167 5 121 5 142 5 301 5 30.0 5 125 5 168 5 313 5 450 5 90 5 39.0 5 336 5 170 5 156 5 190 5 161 5 149 5 133 5 399 5 88 5 157 5 132 5 181 5 32.0 5 176 5 256 5 356 5 130 5 126 5 267 5 95 5 274 5 240 4 260 4 341 4 194 4 248 4 387 4 40.0 4 402 4 257 4 186 4 189 4 325 4 184 4 284 4 283 4 279 4 173 4 65.0 4 74.0 4 306 4 236 4 159 4 43.0 4 220 4 258 4 351 4 166 4 348 4 165 4 377 4 475 4 172 4 61.0 4 385 4 342 4 117 4 44.0 4 403 4 501 4 223 4 227 4 160 4 48.0 4 295 4 146 4 42.0 4 335 4 158 4 33.0 4 417 4 238 4 304 4 452 4 136 4 120 4 207 4 1,665 3 338 3 978 3 311 3 478 3 355 3 251 3 228 3 235 3 254 3 204 3 179 3 50.0 3 483 3 532 3 70.0 3 308 3 791 3 882 3 303 3 331 3 568 3 648 3 494 3 425 3 282 3 275 3 261 3 264 3 272 3 384 3 215 3 553 3 249 3 423 3 277 3 768 3 289 3 63.0 3 528 3 278 3 246 3 435 3 127 3 398 3 334 3 53.0 3 651 3 206 3 59.0 3 265 3 188 3 296 3 133.0 3 324 3 69.0 3 136.0 3 392 3 624 3 326 3 312 3 178 3 266 3 302 3 208 3 101.0 3 252 3 52.0 3 513 3 169 3 504 3 499 3 244 3 411 3 152 3 588 2 354 2 555 2 427 2 932 2 216.0 2 665 2 84.0 2 290 2 239 2 328 2 191 2 777 2 276 2 546 2 408 2 459 2 414 2 401 2 273 2 480 2 343 2 2,452 2 431 2 410 2 232 2 54.0 2 602 2 846 2 544 2 569 2 55.0 2 350 2 573 2 97.0 2 346 2 271 2 177.0 2 263 2 292 2 1,469 2 234 2 877 2 96.0 2 884 2 455 2 102.0 2 57.0 2 562 2 241 2 731 2 37.0 2 349 2 547 2 472 2 609 2 825 2 631 2 314 2 330 2 584 2 225 2 317 2 233 2 841 2 332 2 62.0 2 214 2 132.0 2 606 2 335.0 2 1,179 2 545 2 461 2 556 2 352 2 682 2 1,098 2 46.0 2 754 2 3,896 2 180 2 51.0 2 529 2 205 2 793 2 1,390 2 113 2 5,291 2 2,834 2 424 2 81.0 2 210 2 514 2 1,116 2 1,205 2 507 2 393 2 216 2 574 2 1,806 2 109.0 2 561 2 594 2 659 2 1,178 2 367 2 464 2 165.0 2 458 2 230 2 321 2 262 2 810 2 548 2 652 2 611 2 383 2 72.0 2 140 2 253 2 339 2 650 2 563 2 680 2 543 2 154 2 628 2 627 2 703 2 405 2 285 2 294 2 1,217 2 1,915 2 577 2 347 2 432 2 805 2 394 2 1,015 2 175.0 2 396 2 492 2 767 2 437 2 365 2 270 2 245 2 318 2 570 2 421 2 1,227 2 537 2 344 2 1,468 2 196 2 596 2 620 2 470 2 380 2 222 2 151 2 519 2 593 2 202 2 418 2 128.0 2 154.0 2 1,809 2 428 2 641 2 610 2 1,010 1 933 1 835 1 1,988 1 726 1 1,616 1 189.0 1 899 1 1,052 1 746 1 794 1 782 1 1,021 1 381 1 897 1 560 1 920 1 130.0 1 818 1 745 1 441 1 892 1 316 1 336.0 1 690 1 4,168 1 235.0 1 2,239 1 113.0 1 550 1 362.0 1 1,422 1 1,283 1 498 1 1,771 1 1,344 1 406 1 676 1 1,079 1 377.0 1 476 1 213 1 77.0 1 370 1 661 1 486 1 2,203 1 1,407 1 1,692 1 1,150 1 337 1 1,047 1 1,208 1 3,196 1 769 1 720 1 1,192 1 888 1 1,166 1 1,355 1 1,446 1 721 1 870 1 411.0 1 140.0 1 194.0 1 88.0 1 286.0 1 423.0 1 387.0 1 529.0 1 600.0 1 117.0 1 198.0 1 485.0 1 94.0 1 1,897 1 8,847 1 909 1 4,676 1 479 1 112.0 1 191.0 1 787 1 1,866 1 1,220 1 1,353 1 749 1 2,528 1 1,244 1 4,422 1 2,005 1 3,442 1 950 1 989 1 433 1 2,241 1 1,532 1 2,773 1 579 1 512 1 517 1 736 1 1,016 1 1,564 1 557 1 469 1 1,143 1 587 1 558 1 1,242 1 320 1 2,622 1 422 1 850 1 358 1 2,865 1 832 1 2,574 1 212 1 807 1 1,191 1 1,121 1 827 1 698 1 973 1 986 1 1,551 1 3,291 1 1,850 1 386 1 1,885 1 1,549 1 1,661 1 286 1 1,128 1 1,132 1 366 1 2,016 1 391 1 462 1 345 1 357 1 756 1 554 1 526 1 1,433 1 586 1 1,473 1 1,250 1 771 1 453 1 1,142 1 1,503 1 601 1 1,061 1 664 1 400 1 917 1 1,158 1 1,693 1 2,189 1 1,020 1 820 1 3,046 1 1,596 1 1,853 1 4,276 1 1,377 1 4,015 1 5,396 1 567 1 837 1 642 1 2,061 1 364 1 1,050 1 812 1 200 1 1,105 1 1,051 1 1,049 1 2,069 1 630 1 998 1 407 1 2,852 1 1,832 1 226 1 389.0 1 774 1 647 1 1,045 1 1,040 1 1,032 1 775 1 497 1 867 1 2,778 1 323 1 849 1 765 1 1,681 1 1,374 1 2,658 1 1,022 1 925 1 881.0 1 801.0 1 430 1 603.0 1 520 1 715 1 2,115 1 5,310 1 559 1 2,103 1 862 1 1,766 1 1,139 1 11,201 1 549 1 1,101 1 1,689 1 1,586 1 288 1 1,707 1 301.0 1 259 1 2,335 1 842 1 716 1 2,580 1 1,361 1 625 1 707 1 448 1 4,726 1 371 1 761 1 2,089 1 3,566 1 1,935 1 395 1 5,088 1 723 1 1,958 1 2,251 1 757 1 566.0 1 709 1 1,943 1 1,038 1 1,005 1 748 1 252.0 1 641.0 1 1,000 1 141.0 1 91.0 1 964 1 2,943 1 2,678 1 510 1 4,342 1 340 1 1,601 1 353 1 1,006 1 2,607 1 2,359 1 946 1 1,274 1 1,925 1 389 1 291 1 1,559 1 885 1 3,781 1 5,800 1 1,318 1 1,431 1 297 1 595 1 581 1 379 1 667 1 429 1 535 1 904 1 446 1 585 1 1,983 1 500 1 516 1 447 1 685 1 523 1 1,827 1 1,119 1 1,534 1 2,007 1 3,577 1 66.0 1 73.0 1 67.0 1 80.0 1 106.0 1 49.0 1 126.0 1 139.0 1 1,294 1 86.0 1 253.0 1 75.0 1 147.0 1 736.0 1 114.0 1 508 1 1,923 1 856 1 1,622 1 83.0 1 144.0 1 64.0 1 250 1 443 1 1,379 1 575 1 992 1 965 1 1,371 1 1,314 1 305 1 1,525 1 1,248 1 170.0 1 299.0 1 160.0 1 5,961 1 280 1 1,138 1 374 1 505 1 3,043 1 376 1 1,709 1 1,881 1 955 1 1,103 1 803 1 1,065 1 8,792 1 3,154 1 1,744 1 1,720 1 815 1 1,075 1 1,730 1 654 1 2,129 1 2,075 1 735 1 3,736 1 9,729 1 943 1 2,427 1 373 1 255 1 1,076 1 552 1 645 1 2,301 1 3,317 1 1,487 1 547.0 1 120.0 1 2,790 1 911 1 623 1 677 1 1,295 1 197.0 1 795 1 466 1 660 1 298 1 268 1 511 1 215.0 1 153.0 1 747 1 3,431 1 5,971 1 1,726 1 799 1 2,060 1 3,229 1 1,945 1 319 1 103.0 1 509 1 644 1 213.0 1 331.0 1 166.0 1 548.0 1 822 1 2,157 1 310 1 813 1 463 1 515 1 1,197 1 451 1 858 1 221 1 887 1 851 1 928 1 47.0 1 60.0 1 281.0 1 1,262 1 763 1 1,912 1 804 1 883 1 629 1 434 1 743 1 688 1 893 1 178.0 1 123.0 1 167.0 1 118.0 1 298.0 1 105.0 1 71.0 1 116.0 1 95.0 1 3,352 1 2,964 1 12,396 1 2,630 1 2,191 1 2,615 1 678 1 784 1 1,498 1 2,735 1 1,174 1 426 1 1,758 1 522 1 1,679 1 646 1 1,561 1 3,225 1 2,108 1 3,378 1 2,403 1 829 1 1,920 1 1,324 1 1,774 1 4,855 1 2,640 1 2,283 1 2,329 1 834 1 687 1 2,559 1 506 1 2,186 1 608 1 2,037 1 2,032 1 994 1 1,207 1 988 1 12,860 1 530 1 619 1 368 1 1,538 1 5,260 1 3,557 1 1,084 1 1,475 1 1,230 1 1,464 1 2,487 1 3,063 1 10,732 1 327 1 10,938 1 2,896 1 1,874 1 4,058 1 89.0 1 678.0 1 442 1 2,850 1 2,059 1 329 1 333 1 322 1 224 1 242 1 525 1 477 1 539 1
In [ ]:
df['Rating Count'] = df['Rating Count'].str.replace(',', '').fillna(-1).astype(float)
print_unique_elements(df, ['Rating Count'])
Rating Count: 1.0 194 3.0 174 2.0 169 5.0 121 4.0 118 -1.0 104 7.0 100 11.0 99 12.0 96 15.0 93 6.0 89 10.0 86 16.0 80 13.0 78 21.0 74 18.0 74 9.0 73 8.0 73 17.0 71 22.0 67 20.0 67 14.0 65 25.0 61 24.0 59 19.0 57 26.0 52 35.0 51 23.0 50 29.0 48 33.0 47 30.0 44 34.0 41 28.0 41 31.0 41 27.0 39 40.0 37 36.0 33 39.0 33 45.0 32 41.0 31 38.0 30 48.0 30 44.0 29 32.0 29 43.0 28 63.0 27 37.0 27 56.0 27 53.0 26 46.0 26 42.0 26 47.0 24 49.0 24 79.0 24 50.0 23 51.0 22 62.0 22 61.0 21 60.0 20 54.0 19 57.0 19 64.0 19 101.0 19 52.0 19 78.0 19 70.0 19 66.0 19 59.0 18 91.0 18 81.0 17 73.0 17 58.0 16 84.0 16 75.0 16 55.0 16 92.0 15 93.0 15 82.0 14 65.0 14 68.0 14 153.0 13 89.0 13 131.0 13 99.0 13 96.0 13 74.0 13 114.0 12 77.0 12 71.0 12 80.0 12 94.0 12 76.0 12 97.0 12 107.0 12 177.0 12 72.0 12 102.0 11 67.0 11 69.0 11 86.0 11 122.0 11 197.0 11 175.0 11 108.0 11 110.0 11 123.0 10 145.0 10 115.0 10 87.0 10 103.0 10 144.0 10 109.0 10 198.0 10 83.0 10 105.0 9 124.0 9 219.0 9 139.0 9 162.0 9 106.0 9 128.0 9 118.0 9 143.0 9 116.0 8 133.0 8 185.0 8 112.0 8 141.0 8 135.0 8 100.0 8 138.0 8 201.0 8 104.0 8 293.0 8 182.0 8 150.0 8 174.0 8 111.0 7 300.0 7 247.0 7 136.0 7 209.0 7 85.0 7 195.0 7 218.0 7 147.0 7 199.0 7 98.0 7 137.0 7 192.0 7 132.0 7 164.0 7 211.0 7 231.0 6 129.0 6 243.0 6 301.0 6 335.0 6 287.0 6 155.0 6 203.0 6 307.0 6 167.0 6 126.0 6 229.0 6 217.0 6 163.0 6 134.0 6 309.0 6 336.0 6 183.0 6 88.0 6 193.0 6 170.0 6 237.0 6 187.0 6 130.0 6 165.0 6 95.0 6 171.0 6 412.0 6 267.0 5 256.0 5 121.0 5 313.0 5 189.0 5 161.0 5 176.0 5 274.0 5 450.0 5 399.0 5 148.0 5 194.0 5 90.0 5 156.0 5 377.0 5 181.0 5 117.0 5 387.0 5 119.0 5 120.0 5 168.0 5 142.0 5 157.0 5 166.0 5 125.0 5 356.0 5 190.0 5 149.0 5 160.0 5 248.0 4 385.0 4 284.0 4 216.0 4 158.0 4 154.0 4 306.0 4 238.0 4 146.0 4 417.0 4 331.0 4 240.0 4 295.0 4 207.0 4 283.0 4 279.0 4 257.0 4 452.0 4 173.0 4 227.0 4 341.0 4 235.0 4 215.0 4 351.0 4 325.0 4 252.0 4 304.0 4 186.0 4 348.0 4 260.0 4 236.0 4 258.0 4 159.0 4 423.0 4 172.0 4 342.0 4 220.0 4 411.0 4 178.0 4 475.0 4 402.0 4 223.0 4 403.0 4 184.0 4 501.0 4 548.0 3 264.0 3 324.0 3 425.0 3 529.0 3 272.0 3 338.0 3 384.0 3 478.0 3 244.0 3 152.0 3 435.0 3 768.0 3 289.0 3 513.0 3 308.0 3 277.0 3 547.0 3 398.0 3 334.0 3 179.0 3 282.0 3 326.0 3 651.0 3 278.0 3 191.0 3 246.0 3 532.0 3 568.0 3 275.0 3 483.0 3 303.0 3 253.0 3 254.0 3 791.0 3 978.0 3 261.0 3 648.0 3 206.0 3 311.0 3 553.0 3 296.0 3 494.0 3 499.0 3 641.0 3 208.0 3 188.0 3 266.0 3 140.0 3 624.0 3 127.0 3 392.0 3 265.0 3 169.0 3 113.0 3 251.0 3 312.0 3 302.0 3 504.0 3 249.0 3 528.0 3 355.0 3 228.0 3 204.0 3 1665.0 3 882.0 3 205.0 2 556.0 2 932.0 2 239.0 2 458.0 2 332.0 2 263.0 2 606.0 2 1390.0 2 232.0 2 793.0 2 825.0 2 461.0 2 610.0 2 271.0 2 1098.0 2 841.0 2 180.0 2 432.0 2 349.0 2 573.0 2 1179.0 2 543.0 2 314.0 2 367.0 2 389.0 2 665.0 2 383.0 2 602.0 2 290.0 2 569.0 2 196.0 2 330.0 2 328.0 2 1217.0 2 352.0 2 344.0 2 424.0 2 884.0 2 1469.0 2 611.0 2 846.0 2 555.0 2 877.0 2 777.0 2 214.0 2 5291.0 2 731.0 2 2834.0 2 210.0 2 507.0 2 544.0 2 594.0 2 1205.0 2 347.0 2 472.0 2 545.0 2 609.0 2 1468.0 2 631.0 2 570.0 2 546.0 2 574.0 2 584.0 2 396.0 2 225.0 2 459.0 2 151.0 2 650.0 2 317.0 2 233.0 2 294.0 2 213.0 2 428.0 2 588.0 2 298.0 2 339.0 2 365.0 2 3896.0 2 241.0 2 678.0 2 234.0 2 418.0 2 286.0 2 628.0 2 401.0 2 1809.0 2 1227.0 2 380.0 2 427.0 2 354.0 2 2452.0 2 410.0 2 292.0 2 414.0 2 408.0 2 754.0 2 682.0 2 561.0 2 245.0 2 222.0 2 343.0 2 596.0 2 1178.0 2 652.0 2 405.0 2 285.0 2 767.0 2 202.0 2 563.0 2 703.0 2 620.0 2 562.0 2 627.0 2 492.0 2 810.0 2 421.0 2 470.0 2 736.0 2 464.0 2 1806.0 2 680.0 2 577.0 2 393.0 2 593.0 2 455.0 2 437.0 2 230.0 2 394.0 2 514.0 2 346.0 2 350.0 2 805.0 2 262.0 2 318.0 2 270.0 2 321.0 2 537.0 2 276.0 2 659.0 2 1015.0 2 519.0 2 480.0 2 273.0 2 1915.0 2 1116.0 2 431.0 2 2069.0 1 1051.0 1 1049.0 1 364.0 1 630.0 1 1105.0 1 998.0 1 200.0 1 812.0 1 1050.0 1 992.0 1 353.0 1 407.0 1 1377.0 1 1525.0 1 820.0 1 1020.0 1 2189.0 1 1248.0 1 1693.0 1 1158.0 1 917.0 1 400.0 1 664.0 1 1061.0 1 601.0 1 1503.0 1 1142.0 1 453.0 1 2061.0 1 1250.0 1 1473.0 1 1596.0 1 586.0 1 1853.0 1 771.0 1 726.0 1 642.0 1 837.0 1 1079.0 1 441.0 1 892.0 1 1881.0 1 2239.0 1 3196.0 1 552.0 1 1076.0 1 1344.0 1 1771.0 1 255.0 1 745.0 1 373.0 1 1407.0 1 2203.0 1 2427.0 1 476.0 1 943.0 1 746.0 1 1616.0 1 835.0 1 1446.0 1 406.0 1 933.0 1 782.0 1 9729.0 1 794.0 1 550.0 1 1047.0 1 1355.0 1 899.0 1 567.0 1 5396.0 1 4015.0 1 1433.0 1 870.0 1 661.0 1 3046.0 1 897.0 1 1052.0 1 381.0 1 1923.0 1 920.0 1 1294.0 1 299.0 1 1010.0 1 508.0 1 818.0 1 370.0 1 486.0 1 1692.0 1 1208.0 1 769.0 1 720.0 1 1192.0 1 888.0 1 1166.0 1 305.0 1 1371.0 1 2852.0 1 479.0 1 1128.0 1 1661.0 1 1549.0 1 1885.0 1 386.0 1 1850.0 1 3291.0 1 1551.0 1 973.0 1 1016.0 1 787.0 1 443.0 1 4676.0 1 366.0 1 909.0 1 8847.0 1 1897.0 1 1379.0 1 485.0 1 600.0 1 1622.0 1 575.0 1 1866.0 1 517.0 1 512.0 1 579.0 1 1132.0 1 2016.0 1 4276.0 1 320.0 1 1121.0 1 1191.0 1 250.0 1 807.0 1 212.0 1 2574.0 1 832.0 1 2865.0 1 358.0 1 850.0 1 422.0 1 2622.0 1 1242.0 1 391.0 1 558.0 1 587.0 1 1143.0 1 469.0 1 827.0 1 557.0 1 698.0 1 986.0 1 756.0 1 357.0 1 345.0 1 462.0 1 2773.0 1 1532.0 1 2241.0 1 11201.0 1 647.0 1 774.0 1 881.0 1 801.0 1 603.0 1 1707.0 1 288.0 1 1586.0 1 1689.0 1 965.0 1 1101.0 1 549.0 1 1139.0 1 433.0 1 1766.0 1 862.0 1 560.0 1 2103.0 1 1314.0 1 559.0 1 5310.0 1 2115.0 1 715.0 1 520.0 1 430.0 1 1832.0 1 1045.0 1 1040.0 1 1032.0 1 775.0 1 989.0 1 950.0 1 3442.0 1 2005.0 1 4422.0 1 1244.0 1 2528.0 1 749.0 1 1353.0 1 1220.0 1 554.0 1 1564.0 1 526.0 1 226.0 1 925.0 1 1022.0 1 2658.0 1 1374.0 1 1681.0 1 765.0 1 849.0 1 721.0 1 2778.0 1 867.0 1 497.0 1 1988.0 1 1422.0 1 3736.0 1 525.0 1 322.0 1 5088.0 1 723.0 1 224.0 1 4342.0 1 757.0 1 242.0 1 340.0 1 581.0 1 667.0 1 429.0 1 477.0 1 395.0 1 1084.0 1 1207.0 1 994.0 1 2032.0 1 3225.0 1 1561.0 1 646.0 1 1679.0 1 522.0 1 1758.0 1 1174.0 1 333.0 1 329.0 1 2735.0 1 327.0 1 1230.0 1 1475.0 1 3557.0 1 988.0 1 5260.0 1 1538.0 1 448.0 1 368.0 1 619.0 1 530.0 1 12860.0 1 10938.0 1 1935.0 1 2896.0 1 4726.0 1 1874.0 1 4058.0 1 716.0 1 371.0 1 2089.0 1 442.0 1 2850.0 1 2059.0 1 3566.0 1 2630.0 1 1498.0 1 2487.0 1 1431.0 1 595.0 1 2640.0 1 4855.0 1 1774.0 1 1324.0 1 1920.0 1 12396.0 1 3317.0 1 2335.0 1 2301.0 1 645.0 1 1318.0 1 500.0 1 5800.0 1 3781.0 1 291.0 1 885.0 1 297.0 1 1006.0 1 2607.0 1 2359.0 1 1559.0 1 946.0 1 1274.0 1 2283.0 1 829.0 1 784.0 1 2037.0 1 2615.0 1 535.0 1 904.0 1 2191.0 1 2108.0 1 426.0 1 446.0 1 3378.0 1 585.0 1 379.0 1 834.0 1 1983.0 1 1119.0 1 608.0 1 2186.0 1 516.0 1 447.0 1 685.0 1 506.0 1 2559.0 1 687.0 1 2329.0 1 523.0 1 1827.0 1 1464.0 1 3063.0 1 1150.0 1 803.0 1 3043.0 1 376.0 1 5961.0 1 1709.0 1 1262.0 1 281.0 1 928.0 1 887.0 1 955.0 1 1103.0 1 813.0 1 1065.0 1 1601.0 1 221.0 1 8792.0 1 858.0 1 3154.0 1 451.0 1 856.0 1 1197.0 1 515.0 1 463.0 1 1912.0 1 804.0 1 763.0 1 842.0 1 629.0 1 676.0 1 735.0 1 337.0 1 2075.0 1 2129.0 1 654.0 1 316.0 1 690.0 1 4168.0 1 1283.0 1 1730.0 1 1075.0 1 498.0 1 323.0 1 815.0 1 1925.0 1 362.0 1 1720.0 1 1744.0 1 280.0 1 1138.0 1 374.0 1 505.0 1 1021.0 1 259.0 1 883.0 1 434.0 1 10732.0 1 1945.0 1 2678.0 1 510.0 1 1000.0 1 644.0 1 2251.0 1 509.0 1 761.0 1 1958.0 1 319.0 1 3431.0 1 2580.0 1 1361.0 1 911.0 1 3229.0 1 2060.0 1 799.0 1 1726.0 1 625.0 1 707.0 1 5971.0 1 3352.0 1 747.0 1 2964.0 1 2403.0 1 2790.0 1 623.0 1 743.0 1 268.0 1 688.0 1 893.0 1 3577.0 1 2007.0 1 851.0 1 1534.0 1 709.0 1 310.0 1 2157.0 1 822.0 1 511.0 1 1943.0 1 2943.0 1 1038.0 1 1005.0 1 660.0 1 748.0 1 466.0 1 795.0 1 566.0 1 1295.0 1 1487.0 1 677.0 1 964.0 1 539.0 1
Fill null values of Rating Count with median of Rating Counts in that field and level¶
In [ ]:
df["Rating Count"].replace(-1, np.nan, inplace=True)
grouping_fields = ["Field", "Seller Level"]
def fill_with_group_median(group):
median_value = group["Rating Count"].median()
group["Rating Count"].fillna(median_value, inplace=True)
return group
df["Rating Count"] = df.groupby(grouping_fields)["Rating Count"].transform(lambda x: x.fillna(x.median()))
In [ ]:
print_unique_elements(df, ['Member Since'])
Member Since: May 2020 93 Dec 2020 91 Jun 2020 88 Aug 2020 82 Oct 2020 79 Mar 2021 76 Nov 2020 74 Jun 2021 70 Jul 2021 69 Oct 2021 69 Dec 2023 67 Apr 2020 66 Mar 2020 63 May 2022 60 Jul 2020 60 Dec 2019 60 Jan 2020 59 Jan 2021 58 Aug 2019 58 Aug 2022 58 Nov 2021 57 May 2021 56 May 2023 56 Feb 2021 56 Apr 2022 55 Aug 2023 53 Oct 2023 53 Jan 2023 53 Apr 2023 52 Nov 2023 51 Sep 2022 51 Sep 2020 51 Feb 2022 51 Sep 2021 51 Jul 2019 50 Jan 2022 50 Apr 2021 50 May 2019 49 Feb 2020 48 Nov 2019 46 Jan 2024 45 Jul 2022 44 Nov 2022 44 Jul 2023 44 Mar 2022 44 Jun 2022 43 Aug 2021 43 Sep 2019 42 Feb 2023 42 Mar 2023 40 Apr 2019 40 Oct 2022 39 Sep 2023 38 Jan 2018 38 Jun 2023 37 Mar 2024 37 Dec 2021 37 Oct 2019 36 Feb 2024 36 Jan 2019 35 Feb 2019 34 Jun 2019 31 Mar 2019 31 Sep 2017 31 Mar 2018 30 Dec 2022 30 Oct 2018 30 Sep 2018 29 Jan 2017 29 Apr 2024 27 May 2018 27 Nov 2016 24 Dec 2018 24 Dec 2016 23 Oct 2017 23 Aug 2016 22 Feb 2018 22 Feb 2017 22 Jul 2018 21 Jun 2016 21 Jun 2018 20 May 2017 19 Aug 2017 18 Jul 2016 18 Mar 2017 18 Oct 2015 18 Apr 2018 18 Dec 2017 17 Jun 2017 17 Jul 2017 15 Apr 2016 14 Nov 2018 13 Nov 2017 13 Dec 2014 13 Aug 2018 12 Sep 2016 12 Feb 2016 12 Jun 2015 11 Jul 2015 11 Sep 2015 11 Oct 2016 11 May 2015 10 Apr 2017 10 Nov 2015 10 Feb 2015 10 Mar 2016 10 May 2014 10 Jan 2016 9 Aug 2014 8 Sep 2013 8 Jan 2013 8 Jan 2014 8 Oct 2014 8 May 2016 8 Jul 2014 7 Dec 2015 7 Jan 2015 7 Aug 2015 7 Feb 2014 7 Aug 2013 7 May 2024 6 Feb 2013 6 Oct 2013 5 Mar 2012 5 Mar 2015 5 Apr 2015 4 May 2013 4 Aug 2012 4 Sep 2014 4 Sept 2019 4 Apr 2014 4 Jun 2012 4 Sept 2017 3 Sept 2020 3 Jun 2014 3 Mar 2014 3 Nov 2013 3 May 2012 3 Sept 2021 3 Jun 2013 3 Jan 2012 2 Sep 2012 2 Dec 2011 2 Oct 2011 2 Jul 2012 2 Nov 2014 2 Sept 2013 2 Apr 2012 2 Apr 2013 2 Oct 2012 2 Sept 2022 2 Aug 2011 1 Dec 2013 1 Mar 2011 1 Sept 2016 1 Nov 2012 1 Sept 2018 1
In [ ]:
df['Member Since'] = df['Member Since'].str.replace('Sept', 'Sep')
df["Member Since"] = pd.to_datetime(df["Member Since"], format="%b %Y")
df["Member Since"] = df["Member Since"].dt.strftime("%Y-%m-%d")
In [ ]:
print_unique_elements(df, ['Member Since'])
Member Since: 2020-05-01 93 2020-12-01 91 2020-06-01 88 2020-08-01 82 2020-10-01 79 2021-03-01 76 2020-11-01 74 2021-06-01 70 2021-10-01 69 2021-07-01 69 2023-12-01 67 2020-04-01 66 2020-03-01 63 2019-12-01 60 2022-05-01 60 2020-07-01 60 2020-01-01 59 2019-08-01 58 2022-08-01 58 2021-01-01 58 2021-11-01 57 2023-05-01 56 2021-05-01 56 2021-02-01 56 2022-04-01 55 2020-09-01 54 2021-09-01 54 2023-01-01 53 2023-08-01 53 2022-09-01 53 2023-10-01 53 2023-04-01 52 2022-02-01 51 2023-11-01 51 2021-04-01 50 2022-01-01 50 2019-07-01 50 2019-05-01 49 2020-02-01 48 2019-11-01 46 2019-09-01 46 2024-01-01 45 2022-11-01 44 2022-07-01 44 2022-03-01 44 2023-07-01 44 2022-06-01 43 2021-08-01 43 2023-02-01 42 2019-04-01 40 2023-03-01 40 2022-10-01 39 2023-09-01 38 2018-01-01 38 2024-03-01 37 2021-12-01 37 2023-06-01 37 2024-02-01 36 2019-10-01 36 2019-01-01 35 2017-09-01 34 2019-02-01 34 2019-06-01 31 2019-03-01 31 2018-10-01 30 2018-03-01 30 2018-09-01 30 2022-12-01 30 2017-01-01 29 2024-04-01 27 2018-05-01 27 2016-11-01 24 2018-12-01 24 2016-12-01 23 2017-10-01 23 2018-02-01 22 2017-02-01 22 2016-08-01 22 2018-07-01 21 2016-06-01 21 2018-06-01 20 2017-05-01 19 2017-03-01 18 2015-10-01 18 2017-08-01 18 2016-07-01 18 2018-04-01 18 2017-06-01 17 2017-12-01 17 2017-07-01 15 2016-04-01 14 2018-11-01 13 2016-09-01 13 2014-12-01 13 2017-11-01 13 2018-08-01 12 2016-02-01 12 2015-07-01 11 2015-09-01 11 2016-10-01 11 2015-06-01 11 2014-05-01 10 2017-04-01 10 2015-02-01 10 2016-03-01 10 2013-09-01 10 2015-05-01 10 2015-11-01 10 2016-01-01 9 2014-08-01 8 2014-01-01 8 2014-10-01 8 2013-01-01 8 2016-05-01 8 2014-02-01 7 2014-07-01 7 2015-12-01 7 2015-01-01 7 2015-08-01 7 2013-08-01 7 2024-05-01 6 2013-02-01 6 2013-10-01 5 2012-03-01 5 2015-03-01 5 2012-06-01 4 2015-04-01 4 2013-05-01 4 2012-08-01 4 2014-09-01 4 2014-04-01 4 2012-05-01 3 2014-06-01 3 2014-03-01 3 2013-11-01 3 2013-06-01 3 2012-04-01 2 2011-12-01 2 2013-04-01 2 2012-10-01 2 2012-01-01 2 2012-09-01 2 2014-11-01 2 2012-07-01 2 2011-10-01 2 2011-03-01 1 2011-08-01 1 2013-12-01 1 2012-11-01 1
In [ ]:
print_unique_elements(df , ['Avg Response Time'])
Avg Response Time: 1 hour 2544 2 hours 505 3 hours 300 4 hours 221 5 hours 131 6 hours 90 7 hours 58 8 hours 55 9 hours 46 1 day 45 2 days 37 10 hours 31 11 hours 23 3 days 19 12 hours 18 17 hours 15 16 hours 11 13 hours 10 19 hours 10 21 hours 9 4 days 8 15 hours 8 14 hours 8 23 hours 7 6 days 6 22 hours 5 20 hours 4 12 days 4 18 hours 3 11 days 2 5 days 1 7 days 1 29 days 1 13 days 1
In [ ]:
def convert_to_hours(time_str):
if isinstance(time_str, str):
if 'day' in time_str:
return int(time_str.split(' ')[0]) * 24
elif 'hour' in time_str:
return int(time_str.split(' ')[0])
return time_str
df['Avg Response Time'] = df['Avg Response Time'].apply(convert_to_hours)
print_unique_elements(df , ['Avg Response Time'])
Avg Response Time: 1.0 2544 2.0 505 3.0 300 4.0 221 5.0 131 6.0 90 7.0 58 8.0 55 9.0 46 24.0 45 48.0 37 10.0 31 11.0 23 72.0 19 12.0 18 17.0 15 16.0 11 13.0 10 19.0 10 21.0 9 96.0 8 15.0 8 14.0 8 23.0 7 144.0 6 22.0 5 20.0 4 288.0 4 18.0 3 264.0 2 120.0 1 168.0 1 696.0 1 312.0 1
In [ ]:
print_unique_elements(df, ['Last Delivery'])
Last Delivery: 1 day 605 1 week 450 2 days 373 3 days 261 1 month 210 2 weeks 209 4 days 185 5 days 145 3 weeks 139 about 2 hours 107 2 months 105 about 4 hours 90 about 3 hours 82 about 5 hours 81 about 10 hours 68 6 days 67 about 6 hours 63 about 7 hours 62 about 1 hour 56 about 9 hours 53 about 11 hours 53 about 15 hours 52 7 months 50 about 8 hours 48 about 21 hours 43 4 months 42 3 months 38 about 14 hours 38 about 19 hours 37 about 17 hours 36 1 year 36 about 13 hours 35 about 12 hours 34 5 months 32 about 20 hours 31 about 22 hours 30 about 18 hours 29 about 16 hours 28 4 weeks 27 about 23 hours 27 6 months 23 8 months 23 10 months 22 11 months 15 9 months 10 about 45 minutes 6 2 years 6 about 8 minutes 4 about 49 minutes 4 about 48 minutes 3 about 11 minutes 3 about 33 minutes 3 about 47 minutes 3 about 1 minute 3 about 19 minutes 3 about 52 minutes 3 about 4 minutes 3 about 50 minutes 2 about 57 minutes 2 just now 2 about 6 minutes 2 about 44 minutes 2 about 28 minutes 2 about 32 minutes 2 about 24 minutes 2 about 2 minutes 2 about 46 minutes 2 about 54 minutes 2 about 12 minutes 2 about 5 minutes 2 about 23 minutes 2 about 21 minutes 2 about 37 minutes 2 about 38 minutes 2 about 53 minutes 2 about 55 minutes 2 about 7 minutes 2 3 years 1 about 25 minutes 1 about 42 minutes 1 about 18 minutes 1 12 months 1 about 36 minutes 1 about 9 minutes 1 about 15 minutes 1 about 35 minutes 1 about 34 minutes 1 about 16 minutes 1 about 17 minutes 1 about 20 minutes 1 about 29 minutes 1
In [ ]:
def convert_to_days(val):
if pd.isnull(val):
return np.nan
try:
quantity, unit = val.split()
quantity = int(quantity)
if unit in ['day', 'days']:
return quantity
elif unit in ['week', 'weeks']:
return quantity * 7
elif unit in ['month', 'months']:
return quantity * 30
elif unit in ['year', 'years']:
return quantity * 365
else:
return 0
except :
return 0
df['Last Delivery'] = df['Last Delivery'].apply(convert_to_days)
print_unique_elements(df, ['Last Delivery'])
Last Delivery: 0.0 1273 1.0 605 7.0 450 2.0 373 3.0 261 30.0 210 14.0 209 4.0 185 5.0 145 21.0 139 60.0 105 6.0 67 210.0 50 120.0 42 90.0 38 365.0 36 150.0 32 28.0 27 180.0 23 240.0 23 300.0 22 330.0 15 270.0 10 730.0 6 1095.0 1 360.0 1
In [ ]:
df['Last Delivery'].isnull().sum()
Out[Â ]:
1574
In [ ]:
df['Language'] = df['Language'].str.replace('I speak ', '')
print_unique_elements(df, ['Language'])
Language: English 1905 English, Spanish 324 Urdu, English 269 English, French 133 English, German 113 Bengali, English 102 English, Urdu 94 Hindi, English 80 English, Italian 71 English, Hindi 69 Bengali, English, Hindi 44 English, Portuguese 44 English, Indonesian 44 English, Russian 43 English, Arabic 42 English, Arabic, French 41 English, Chinese 41 English, Spanish, French, German 40 Urdu, English, Hindi 39 English, Spanish, German, French 38 Spanish, English 36 English, Ukrainian 34 English, Spanish, French 31 English, French, Spanish 29 English, Turkish 29 English, Tagalog 29 English, French, German, Spanish 27 English, German, Spanish, French 27 English, Japanese 26 English, Portuguese, Spanish 25 Sinhala, English 23 English, Urdu, Hindi 23 English, Hebrew 21 English, Greek 21 English, German, French, Spanish 20 Urdu, English, Punjabi 20 English, Polish 19 English, German, Spanish 19 English, Spanish, Portuguese 19 English, Dutch 18 English, German, French 18 English, Spanish, German 17 Urdu, Punjabi, English 17 Urdu, English, Spanish 16 English, Russian, Ukrainian 16 English, French, Spanish, German 16 English, Spanish, Italian 15 English, Italian, Spanish 15 English, Arabic, French, Spanish 14 Urdu, English, Arabic 14 English, Sinhala 14 English, Bengali 14 Urdu, Pashto, English 13 English, Romanian 13 English, French, Arabic 12 English, Swedish 12 English, French, German 12 English, Serbian 12 English, Urdu, Punjabi 12 English, Italian, French, Spanish 11 Urdu, Hindi, English 11 Urdu, English, French 10 Urdu, English, French, German 10 Gujarati, English, Hindi 10 Tamil, English 10 Hindi, English, Urdu 10 English, Swahili 9 English, Gujarati, Hindi 9 Bengali, English, Spanish, German 9 English, Bengali, Hindi 9 English, Hindi, Gujarati 9 Urdu, English, Chinese 9 English, French, Spanish, Italian 9 Spanish 9 English, Bulgarian 9 English, Spanish, French, Italian 9 Urdu, English, Hindi, Punjabi 9 Indonesian, English 8 English, Slovenian 8 English, Urdu, French, Spanish 8 English, Dutch, German, French 8 Urdu, English, Spanish, French 8 English, Korean 8 English, Vietnamese 8 Hindi, Urdu, English 8 English, German, Italian, Spanish 7 Ukrainian, English 7 Urdu, Punjabi, English, Arabic 7 English, Ukrainian, Polish 7 English, Italian, Spanish, French 7 Urdu, English, German 6 English, Hungarian, German 6 English, Albanian 6 English, German, Spanish, Dutch 6 Bengali, Hindi, English 6 Urdu, Punjabi, English, Hindi 6 English, Malay 6 Urdu, English, Punjabi, Hindi 6 French, English 6 Bengali, English, Hindi, Urdu 6 English, Urdu, Pashto 6 English, Spanish, German, Italian 6 English, German, French, Italian 6 English, Italian, Spanish, German 5 English, Estonian, Spanish 5 English, Armenian, German, Russian 5 English, Persian 5 English, Greek, Russian 5 English, Ukrainian, Russian 5 English, Portuguese, Spanish, Italian 5 English, Spanish, Italian, French 5 English, Ukrainian, Russian, Spanish 5 Hindi, Gujarati, English 5 Telugu, English 5 Russian, Ukrainian, English 5 English, Italian, French 5 English, Hindi, Spanish, French 5 English, Dutch, German 5 English, Arabic, French, German 5 Italian, English 5 English, Hindi, Punjabi 5 Bengali, English, Hebrew, Hindi 5 Urdu, Punjabi, Hindi, English 5 English, French, Italian 5 Bengali, English, Spanish 5 Bengali, English, German 5 English, Portuguese, French 5 English, Spanish, Italian, German 4 Russian, English 4 Chinese, English 4 English, German, French, Thai 4 English, Swahili, Kikuyu 4 Urdu, English, Spanish, German 4 English, Indonesian, Malay, German 4 English, Polish, Spanish 4 French, German, Spanish, English 4 English, Nepali 4 English, German, Spanish, Arabic 4 Bengali, English, Hindi, Spanish 4 Urdu, English, Hindi, Spanish 4 English, Spanish, Catalan 4 English, Portuguese, German, French 4 English, Croatian 4 English, Dutch, French 4 English, Urdu, German 4 English, Hungarian 4 German, English 4 English, Urdu, Arabic 4 English, Spanish, Dutch, German 4 English, Georgian, Russian 4 Bengali, English, Hindi, Hebrew 4 English, Hindi, French, Spanish 4 English, Slovak 4 Urdu, English, German, French 4 English, French, Arabic, Spanish 4 English, Indonesian, Malay 4 Bengali, English, Spanish, French 4 English, Urdu, Spanish 4 English, Russian, French 4 English, Spanish, Portuguese, Turkish 4 English, Slovenian, Croatian, Latin 4 Urdu, English, Hindi, French 4 English, Dutch, Spanish, German 4 Hindi, English, Spanish, French 4 English, Urdu, Hindi, Punjabi 4 English, Hindi, Marathi 4 English, Russian, German 4 English, Macedonian, Serbian 3 English, Greek, Spanish 3 English, Urdu, German, French 3 English, Ukrainian, Russian, Polish 3 English, Urdu, Arabic, German 3 Urdu, English, Arabic, French 3 Bengali, English, German, French 3 English, Spanish, Galician 3 English, German, Greek 3 Hindi, Bengali, English 3 English, Thai 3 English, Hindi, German, Spanish 3 English, Italian, French, German 3 English, Korean, German, Thai 3 Ukrainian, Russian, English 3 English, Afrikaans 3 English, Urdu, Spanish, French 3 English, Norwegian Bokmål, Swedish, Danish 3 English, German, French, Korean 3 Turkish, English 3 English, Turkish, German 3 Urdu, English, Punjabi, Arabic 3 English, German, Thai, Spanish 3 English, Urdu, Spanish, German 3 English, French, Dutch 3 Urdu, English, German, Spanish 3 English, German, Italian 3 English, Hebrew, French, Spanish 3 Punjabi, English, Hindi 3 Hindi, Punjabi, English 3 English, Indonesian, Chinese 3 English, Arabic, Spanish, French 3 English, Danish, Swedish, German 3 English, Dutch, French, German 3 Arabic, English 3 English, French, Spanish, Dutch 3 Urdu, English, Hindi, Arabic 3 English, Serbian, Portuguese, Spanish 3 English, French, Russian, German 3 English, Hungarian, Romanian 3 English, Chinese, German 3 English, French, German, Italian 3 Urdu, Hindi, English, Punjabi 3 English, Serbian, German 3 English, Portuguese, French, Spanish 3 Hindi, English, French 3 English, Arabic, German, French 3 English, Hindi, Urdu 3 Yoruba, English 3 English, German, French, Arabic 3 English, Portuguese, Spanish, French 3 English, Yoruba 3 English, Tamil 3 English, Arabic, German, Spanish 3 German, French, Spanish, English 3 English, Sindhi, Urdu, Hindi 2 Urdu, English, Arabic, Spanish 2 English, Russian, Ukrainian, Polish 2 Portuguese, English 2 Sindhi, English, Urdu 2 English, Bengali, Hindi, Urdu 2 English, Georgian 2 English, Portuguese, Spanish, German 2 English, Russian, Polish, French 2 English, Portuguese, German, Spanish 2 English, Spanish, Portuguese, German 2 English, Serbian, Russian 2 English, Danish 2 Bengali, English, Arabic 2 Hindi, English, Punjabi 2 Urdu, English, French, Spanish 2 English, German, Western Frisian 2 German, English, French 2 English, Latvian, Russian 2 English, Finnish, Swedish 2 Sinhala, English, German 2 English, Chinese, Thai, Lao 2 English, Chinese, Malay, Japanese 2 English, Spanish, Russian, French 2 English, Russian, Chinese 2 English, Greek, Turkish, Arabic 2 English, Spanish, French, Arabic 2 English, Urdu, French, German 2 Bengali, English, Hindi, Arabic 2 English, French, Danish 2 English, Turkish, German, French 2 English, Italian, Greek 2 English, Spanish, Catalan, French 2 English, Indonesian, German, French 2 English, Macedonian, Bulgarian, Serbian 2 Belarusian, English 2 Bengali, English, Hindi, German 2 Urdu, Sindhi, English, Punjabi 2 English, Ukrainian, German 2 English, Spanish, Hebrew, German 2 English, Chinese, Japanese 2 English, Spanish, German, Chinese 2 English, Spanish, Italian, Portuguese 2 English, Finnish 2 English, Hindi, Nepali, Bengali 2 English, Russian, Ukrainian, German 2 English, French, German, Portuguese 2 English, Russian, Hebrew 2 English, Romanian, Italian 2 English, Russian, Belarusian 2 English, Arabic, Spanish 2 English, Lithuanian 2 English, Norwegian Bokmål, Spanish 2 English, Estonian 2 English, German, Turkish, Indonesian 2 English, Armenian, Russian 2 Tamil, English, Sinhala, Malayalam 2 English, Croatian, Italian 2 English, Polish, Russian 2 English, Greek, French 2 English, Spanish, Hindi 2 English, French, Italian, Spanish 2 English, Italian, Portuguese 2 English, Dutch, Spanish, French 2 English, French, Chinese 2 English, Sinhala, Tamil 2 English, Russian, Croatian, Serbian 2 English, Russian, Ukrainian, Czech 2 Business plans 2 English, French, Portuguese, German 2 English, Ukrainian, Polish, Italian 2 English, French, Italian, German 2 English, Urdu, French 2 English, Yoruba, Spanish 2 English, French, Bulgarian 2 English, Romanian, Russian 2 English, Spanish, Japanese 2 English, Hebrew, Spanish 2 English, Japanese, German 2 English, Italian, German, Spanish 2 English, Slovenian, Croatian, German 2 English, Spanish, Russian, German 2 Norwegian Bokmål, English 2 English, German, Arabic, French 2 Urdu, English, French, Chinese 2 English, Hindi, Marathi, Gujarati 2 English, Hausa, Igbo 2 English, Punjabi, Urdu 2 English, Hindi, Nepali 2 English, Italian, Romanian 2 English, German, Hindi 2 English, Urdu, Pashto, Hindi 2 Pashto, Urdu, English 2 Urdu, English, Arabic, German 2 Thai, Korean, English, Urdu 2 Bengali, English, Spanish, Arabic 2 English, German, Arabic, Spanish 2 English, Serbian, Croatian, Bosnian 2 Hindi, English, German, Spanish 2 English, Spanish, Dutch, Italian 2 English, Kannada, Hindi 2 Spanish, French, German, English 2 Bengali, English, Italian, Hindi 2 Bengali, English, German, Spanish 2 Bengali, English, French, German 2 Urdu, English, Spanish, Arabic 2 English, German, Spanish, Italian 2 English, German, Russian, French 2 English, Urdu, Italian, German 2 English, French, Urdu 2 English, Thai, Korean, Indonesian 2 Bengali, English, French, Spanish 2 English, French, German, Arabic 2 English, Spanish, Ukrainian 2 English, German, Turkish 2 Hindi, English, Korean, Thai 2 English, Urdu, Spanish, Arabic 2 English, Indonesian, Javanese 2 Arabic 2 English, German, Thai 2 English, Russian, Italian 2 English, Igbo 2 French, English, German, Spanish 2 English, Thai, Korean 2 Urdu, Sindhi, English 2 Urdu, English, Spanish, Russian 2 Urdu, English, Arabic, Chinese 2 English, Urdu, Hindi, Arabic 2 English, German, Russian 2 Pashto, English, Urdu 2 English, Albanian, Greek, Italian 2 Hindi, English, Spanish 2 English, German, Croatian 2 English, German, Dutch 2 English, German, Spanish, Russian 2 English, Urdu, French, Arabic 2 English, Hebrew, Russian 2 Sindhi, English, Urdu, Hindi 2 Vietnamese, English 2 Russian, Ukrainian, English, Polish 2 English, German, French, Bengali 1 French, Spanish, German, Chinese 1 English, Urdu, German, Hindi 1 English, Czech, Italian, German 1 English, Spanish, Georgian 1 English, Hindi, Arabic, Spanish 1 English, Hindi, Spanish, Arabic 1 English, Arabic, Urdu, Chinese 1 English, German, French, Dutch 1 English, German, Danish 1 Spanish, English, French 1 Urdu, Sindhi, Hindi, English 1 Polish, English, German 1 English, Indonesian, Dutch, German 1 English, Greek, Russian, German 1 French, Italian, Urdu, English 1 German, Spanish, English 1 English, German, Russian, Arabic 1 English, Urdu, German, Korean 1 Bengali, English, Hindi, French 1 Sindhi, Urdu, Hindi, English 1 Urdu, Hindi, English, French 1 Abkhazian, Chinese, Spanish, English 1 Urdu, English, Pashto 1 Urdu, English, Arabic, Hindi 1 Urdu, Pashto, Hindi, English 1 Russian, English, German 1 English, Turkish, Russian, Polish 1 English, Croatian, Bosnian, German 1 English, Polish, German, Spanish 1 English, Uzbek 1 English, Romanian, German 1 English, Persian, Kurdish, Turkish 1 Spanish, Galician, Portuguese, English 1 English, Albanian, Spanish, Chinese 1 English, Italian, Hungarian 1 Urdu, English, Turkish 1 English, Bengali, Hindi, Malay 1 Urdu, English, French, Arabic 1 English, Bengali, German, Finnish 1 English, German, French, Norwegian Bokmål 1 English, Bengali, Spanish, German 1 English, Spanish, German, Hebrew 1 German, Hebrew, French, English 1 Bengali, Dutch, Spanish, English 1 English, Romanian, Hungarian 1 Arabic, English, French 1 English, Spanish, Portuguese, French 1 Hindi, English, Arabic 1 English, Spanish, Hebrew, French 1 Urdu, English, Hindi, German 1 English, Hindi, Punjabi, Spanish 1 Bengali, Hindi, English, Urdu 1 English, Uzbek, Russian, Korean 1 Urdu, English, German, Italian 1 English, French, Arabic, German 1 English, Turkish, Spanish, French 1 Urdu, English, Pashto, Punjabi 1 Telugu, English, Hindi, Kannada 1 English, Russian, Romanian, Greek 1 English, Russian, Ukrainian, Belarusian 1 English, Turkish, Russian 1 Arabic, English, German, French 1 English, French, Arabic, Russian 1 English, Hindi, French, Arabic 1 English, Hindi, Portuguese 1 Spanish, English, German, Italian 1 Nepali, Hindi, English 1 English, Urdu, Sindhi 1 English, Urdu, Dutch 1 Hindi, English, Bengali 1 Arabic, German, English, French 1 English, Chinese, Spanish, French 1 Urdu, Punjabi, English, Spanish 1 Flutter 1 English, Urdu, German, Spanish 1 Bengali, English, Spanish, Hindi 1 Urdu, English, Russian, Dutch 1 English, French, Spanish, Russian 1 English, Arabic, Urdu 1 English, Russian, French, Turkish 1 English, Nepali, Hindi 1 Bengali, English, German, Hebrew 1 Bengali, English, Chinese, German 1 French, German, English 1 English, Spanish, Italian, Russian 1 English, German, Turkish, French 1 Hindi, Gujarati, Tamil, English 1 English, German, Portuguese 1 English, French, Dutch, Italian 1 English, Arabic, French, Dutch 1 English, Bengali, Spanish 1 English, Croatian, German, Swedish 1 English, Hindi, German, Italian 1 English, German, Spanish, Hebrew 1 Urdu, English, Spanish, Chinese 1 English, German, Spanish, Swedish 1 French, Arabic, Spanish, English 1 English, Hebrew, Spanish, German 1 English, Turkmen, Russian 1 English, Ukrainian, Italian 1 English, Danish, French, German 1 Spanish, Urdu, English, Portuguese 1 English, Spanish, French, Chinese 1 English, Macedonian, Serbian, Spanish 1 English, Italian, German 1 Russian, English, Polish 1 Spanish, Arabic, German, English 1 English, French, Spanish, Hindi 1 English, Russian, Spanish, French 1 English, Romanian, French 1 English, Swahili, Spanish, German 1 English, Swedish, Spanish, French 1 English, Bulgarian, German, Spanish 1 Haitian, French, English 1 English, Turkish, French 1 English, French, Yoruba 1 English, Serbian, Croatian, Russian 1 English, Spanish, Portuguese, Armenian 1 English, Polish, Japanese 1 English, Kinyarwanda, French 1 Urdu, German, English 1 English, German, Persian, Chinese 1 English, Persian, Arabic, Polish 1 English, Bulgarian, German 1 English, Russian, Spanish, Ukrainian 1 English, Spanish, French, Dutch 1 Sinhala, English, French, Spanish 1 English, Hebrew, French 1 English, Tamil, Malayalam 1 English, Czech 1 English, Spanish, French, Japanese 1 English, French, German, Icelandic 1 English, Japanese, Hebrew 1 English, Tagalog, Spanish, Italian 1 English, Mongolian, Albanian 1 Urdu, English, Spanish, Hindi 1 Hindi, English, Marathi 1 Arabic, Urdu, English, Pashto 1 English, Swedish, Italian, German 1 English, Hindi, Dutch 1 French, Spanish, English 1 English, Russian, Ukrainian, Spanish 1 Arabic, English, French, Spanish 1 Igbo, English, German 1 English, Urdu, Russian, Spanish 1 English, Yoruba, French 1 Spanish, English, Portuguese 1 English, Interlingua, French 1 Kannada, English, Hindi, Marathi 1 English, Spanish, Dutch, Portuguese 1 English, Hebrew, Lithuanian 1 English, Spanish, French, Hebrew 1 English, Japanese, German, Italian 1 English, Hindi, German 1 Urdu, English, Portuguese, Spanish 1 English, Hindi, Malayalam, Tamil 1 Bengali, Spanish, English, Hindi 1 Dutch, Arabic, English, French 1 German, French, Albanian, English 1 English, Faroese, German 1 Hindi, French, English, German 1 German, English, French, Italian 1 English, Chinese, Spanish, Arabic 1 English, Arabic, Spanish, Russian 1 English, Urdu, Arabic, Spanish 1 English, Tamil, Telugu 1 Real estate analysis 1 English, German, Greek, Latin 1 English, Korean, Chinese, Japanese 1 English, Arabic, Turkish, Russian 1 English, Dutch, German, Chinese 1 Bengali, English, Hindi, Korean 1 English, French, Persian, Spanish 1 English, Hindi, Bengali 1 English, Urdu, Italian, French 1 English, Maltese, Italian, Turkish 1 English, Portuguese, German 1 English, Dutch, German, Spanish 1 English, Spanish, French, Russian 1 English, Croatian, German, Slovenian 1 Portuguese, English, Spanish, French 1 English, Swahili, Chinese 1 English, Swedish, Spanish 1 English, German, Dutch, Indonesian 1 Spanish, Arabic 1 English, Greek, Italian 1 English, Italian, French, Arabic 1 English, Spanish, Arabic, Chinese 1 English, Malayalam, Hindi, Tamil 1 English, Ukrainian, Hebrew 1 English, Urdu, Chinese, Spanish 1 Tamil, English, Hindi 1 Tamil, English, French, German 1 English, Serbian, Croatian, Macedonian 1 English, Spanish, French, Hindi 1 English, Portuguese, French, German 1 English, Kazakh, Ukrainian 1 Hindi, Marathi, English 1 Marathi, English, Hindi 1 English, German, Spanish, Finnish 1 English, Indonesian, Spanish 1 English, Welsh 1 English, Portuguese, Japanese, Spanish 1 French, German, Portuguese, English 1 English, Chinese, Russian, German 1 English, Ukrainian, Russian, Slovak 1 English, Khmer 1 English, Greek, German 1 English, Slovak, Czech, German 1 English, Spanish, French, Portuguese 1 English, French, Haitian, Spanish 1 English, German, Luxembourgish, French 1 Swedish, English 1 English, Dutch, Portuguese, French 1 English, Swedish, German 1 English, Spanish, Latin, Portuguese 1 English, Polish, German, French 1 English, Irish 1 English, Arabic, Chinese, French 1 Turkish 1 English, Greek, Latin 1 English, Kazakh, Russian 1 English, Slovenian, Bosnian, Spanish 1 English, Chinese, Indonesian 1 English, Vietnamese, Korean 1 English, Greek, French, Spanish 1 English, Finnish, Swedish, Spanish 1 English, Chinese, French 1 English, French, Hebrew 1 English, Bulgarian, Spanish, Russian 1 English, Scottish Gaelic 1 Data analysis 1 Hindi, English, Spanish, German 1 English, Ukrainian, German, Spanish 1 English, Korean, Thai, Spanish 1 English, Albanian, Spanish 1 English, Hebrew, Italian, French 1 English, Polish, Spanish, German 1 English, Dutch, Portuguese, Korean 1 English, Norwegian Bokmål 1 English, Serbian, Macedonian, German 1 Telugu, Hindi, English 1 English, Serbian, Bulgarian 1 English, Korean, Arabic 1 Urdu, English, Dutch 1 Thai, Korean, Spanish, English 1 English, Tagalog, German 1 Hindi, Tamil, English 1 English, Serbian, Spanish 1 English, German, Spanish, Thai 1 Urdu, English, French, Italian 1 English, Marathi, Hindi, German 1 English, Serbian, Croatian, German 1 English, Romanian, Russian, French 1 Urdu 1 English, Lithuanian, Russian, Ukrainian 1 English, Indonesian, Italian 1 Tagalog, English, Spanish 1 English, Urdu, Punjabi, Pashto 1 English, Turkish, Urdu, Hindi 1 English, Slovenian, Italian 1 English, Hindi, Gujarati, Marathi 1 English, Thai, Indonesian, Korean 1 Thai, English, Korean, Indonesian 1 English, Afrikaans, Dutch 1 Bulgarian, English 1 English, Hebrew, Russian, Korean 1 English, Haitian 1 English, Arabic, Spanish, German 1 English, Malay, Chinese, Japanese 1 English, Arabic, Turkish 1 Punjabi, Urdu, English, Hindi 1 English, Japanese, Chinese 1 English, Spanish, Italian, Esperanto 1 Urdu, Arabic, English 1 Indonesian, Thai, English, Korean 1 Russian, Belarusian, English, French 1 English, Korean, Spanish, French 1 English, Slovak, Czech 1 English, Arabic, French, Japanese 1 English, Kazakh, Turkish 1 Punjabi, Urdu, English, German 1 English, Spanish, Urdu, French 1 English, Korean, Italian, Thai 1 English, Latvian 1 Bulgarian, English, Russian 1 Thai 1 English, Romanian, Russian, Ukrainian 1 English, French, German, Thai 1 English, Ukrainian, Polish, Russian 1 English, German, Urdu 1 Thai, English, Korean, Spanish 1 English, Ukrainian, German, Polish 1 English, Croatian, Russian, Bulgarian 1 English, Spanish, German, Korean 1 Urdu, Sindhi, English, Arabic 1 English, Belarusian, Ukrainian 1 Spanish, French, Western Frisian, English 1 English, Turkish, German, Spanish 1 Ukrainian, English, Polish 1 English, Hebrew, Spanish, French 1 English, Urdu, Arabic, Dutch 1 French, Hebrew, English 1 English, German, Hebrew, Chinese 1 English, Tagalog, Chinese 1 English, Spanish, Russian 1 Bengali, Hindi, Urdu, English 1 English, Urdu, Chinese, Malay 1 English, Slovak, French 1 English, Sinhala, Japanese 1 English, Urdu, Spanish, Portuguese 1 Bengali, English, Hebrew, Arabic 1 Urdu, Hindi, Punjabi, English 1 Turkish, English, German 1 English, Russian, Turkish, German 1 French, English, Hebrew 1 English, Italian, Dutch 1 Urdu, English, Hindi, Bengali 1 English, Hebrew, Italian 1 English, Japanese, French 1 English, Serbian, Slovak 1 Uzbek, Russian, English 1 English, Hindi, Spanish, German 1 English, Indonesian, Japanese, Chinese 1 English, Albanian, Italian 1 Arabic, English, Spanish, German 1 English, Italian, Arabic, Spanish 1 English, Hindi, German, French 1 English, Korean, Thai 1 Hindi, English, Thai, Korean 1 German, English, Serbian 1 English, Arabic, Russian, French 1 English, Vietnamese, Korean, Japanese 1 Urdu, Punjabi, English, French 1 Hindi, English, French, German 1 English, Urdu, Thai, French 1 English, Greek, Albanian 1 English, Spanish, Portuguese, Catalan 1 English, French, Urdu, Hindi 1 English, Spanish, Hebrew 1 English, Spanish, Arabic, French 1 English, French, German, Korean 1 English, Spanish, German, Swedish 1 Japanese, English, German 1 Hindi, Urdu, English, Spanish 1 English, Ukrainian, German, Russian 1 English, Dutch, Italian, Spanish 1 Tamil, English, Arabic, Urdu 1 English, German, Russian, Latin 1 Bengali, English, Hindi, Assamese 1 English, Russian, French, Spanish 1 Malayalam, English, Hindi, Tamil 1 Hindi, English, Spanish, Urdu 1 English, Serbian, German, Chinese 1 Bengali, English, Spanish, Hebrew 1 English, Bulgarian, Russian 1 Urdu, English, Persian 1 Urdu, English, Pashto, Hindi 1 English, Urdu, Chinese 1 English, Hindi, Telugu, Oriya 1 Data science 1 English, Turkish, Armenian 1 English, Hindi, Marathi, German 1 English, Italian, French, Dutch 1 English, Hebrew, Bengali, Hindi 1 English, Chinese, Russian, Ukrainian 1 English, Yoruba, French, Arabic 1 English, Spanish, Estonian 1 English, German, Romanian 1 English, Norwegian Bokmål, Norwegian Nynorsk 1 English, French, Spanish, Arabic 1 English, Italian, Korean, Spanish 1 German 1 English, Spanish, Portuguese, Italian 1 Telugu, English, Hindi 1 English, Dutch, Swahili, French 1 English, Hindi, Malayalam, Kannada 1 English, French, Korean, German 1 English, French, German, Dutch 1 English, Italian, German, French 1 Punjabi, English, Urdu, Sindhi 1 English, Malay, Chinese 1 English, Dutch, Hebrew, Spanish 1 English, Japanese, Tagalog, Korean 1 English, Bosnian, Spanish, Norwegian 1 Swahili, English 1 Spanish, Catalan, Italian, Russian 1 English, German, Portuguese, Spanish 1 English, Urdu, Punjabi, Hindi 1 English, Malayalam, Hindi, Punjabi 1 English, French, Korean, Japanese 1 English, French, Swedish 1 French, Spanish 1 Russian, Turkish, Azerbaijani, English 1 English, Italian, Portuguese, Spanish 1 English, Tigrinya, Amharic 1 English, Hindi, Marathi, Punjabi 1 Chinese, German, English 1 English, Tamil, Sinhala 1 English, Spanish, Catalan, Portuguese 1 English, Urdu, Hindi, French 1 English, Slovak, Czech, Polish 1 Romanian, English 1 Hindi, English, Kannada 1 English, Slovak, Czech, Spanish 1 English, Turkish, French, German 1 Italian 1 English, Lithuanian, Russian 1 English, Italian, Spanish, Czech 1 English, Polish, French, German 1 English, Italian, Russian, German 1 English, Romanian, Spanish, German 1 English, Serbian, Spanish, German 1 Russian, Romanian 1 Punjabi, English, Urdu, Hindi 1 English, Hindi, Arabic 1 English, Ukrainian, Russian, German 1 Russian, Romanian, English 1 English, Igbo, Yoruba, Hausa 1 English, German, Polish 1 English, French, German, Luxembourgish 1 Hindi, Gujarati, English, Urdu 1 English, Indonesian, Japanese 1 English, French, Japanese, Spanish 1 English, Polish, German 1 English, Bulgarian, Polish, German 1 English, Hindi, Kannada 1 English, Xhosa 1 English, Turkish, Spanish, Russian 1 English, Hindi, Sanskrit, Spanish 1 English, Bengali, Spanish, French 1 English, Hindi, Spanish, Hebrew 1 Hindi, English, Gujarati, Assamese 1 Assamese, English, Hindi, Bengali 1 Arabic, Spanish, English 1 English, French, Sinhala 1 English, Bengali, Urdu, Hindi 1 English, Georgian, Turkish, Spanish 1 Hindi, English, German, Urdu 1 English, Indonesian, Korean 1 Bengali, English, Spanish, Russian 1 English, Urdu, Turkish 1 English, Dutch, Spanish 1 Urdu, English, Dutch, French 1 English, Bengali, German 1 English, German, Turkish, Arabic 1 French 1 Hindi, Telugu, English 1 English, Russian, Latvian 1 English, Bulgarian, Russian, Romanian 1 English, French, Italian, Arabic 1 English, Azerbaijani, Turkish, Russian 1 English, Romanian, Persian 1 English, Spanish, Latin 1 Sinhala 1 English, French, Portuguese 1 Greek, Romanian, English 1 English, Italian, German, Russian 1 English, German, Japanese, Chinese 1 English, Hindi, Tamil, Telugu 1 English, French, Japanese 1 Italian, Spanish, English, Portuguese 1 English, German, Hindi, French 1 Spanish, Portuguese 1 English, Italian, Spanish, Russian 1 Italian, English, Spanish, German 1 English, Spanish, Russian, Armenian 1 English, German, Swedish, French 1 Spanish, Portuguese, English 1 English, Indonesian, Turkish 1 English, Spanish, German, Hindi 1 Urdu, English, Punjabi, Spanish 1 English, Chinese, Hungarian 1 English, Turkish, Japanese, German 1 Spanish, German, English 1 English, Korean, German, Spanish 1 Bengali, Hebrew, Hindi, English 1 English, Hindi, Tagalog, Spanish 1 English, Hindi, French, German 1
Encode languages with more Than %1 repeat in all records using one-hot-encoding¶
In [ ]:
df.loc[df['Language'].notna(), 'Language'] = df['Language'].dropna().str.split(',').apply(lambda x: ','.join(i.strip() for i in x))
df_encoded = df['Language'].str.get_dummies(sep=',')
percentage = df_encoded.mean()
cols_to_drop = percentage[percentage < 0.01].index
df_encoded = df_encoded.drop(cols_to_drop, axis=1)
df = pd.concat([df, df_encoded], axis=1)
df_encoded.info()
<class 'pandas.core.frame.DataFrame'> Index: 5922 entries, 0 to 6261 Data columns (total 18 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Arabic 5922 non-null int64 1 Bengali 5922 non-null int64 2 Chinese 5922 non-null int64 3 Dutch 5922 non-null int64 4 English 5922 non-null int64 5 French 5922 non-null int64 6 German 5922 non-null int64 7 Hebrew 5922 non-null int64 8 Hindi 5922 non-null int64 9 Indonesian 5922 non-null int64 10 Italian 5922 non-null int64 11 Portuguese 5922 non-null int64 12 Punjabi 5922 non-null int64 13 Russian 5922 non-null int64 14 Spanish 5922 non-null int64 15 Turkish 5922 non-null int64 16 Ukrainian 5922 non-null int64 17 Urdu 5922 non-null int64 dtypes: int64(18) memory usage: 879.0 KB
Drop Language Column¶
In [ ]:
df.drop('Language', axis=1,inplace=True)
In [ ]:
print_unique_elements(df, ['Order in Queue'])
Order in Queue: 1 order in queue 822 2 orders in queue 443 3 orders in queue 249 4 orders in queue 159 5 orders in queue 109 6 orders in queue 85 7 orders in queue 50 8 orders in queue 44 11 orders in queue 31 10 orders in queue 29 9 orders in queue 20 12 orders in queue 15 13 orders in queue 11 16 orders in queue 9 14 orders in queue 7 18 orders in queue 7 24 orders in queue 6 19 orders in queue 6 15 orders in queue 6 22 orders in queue 6 31 orders in queue 3 17 orders in queue 3 25 orders in queue 3 21 orders in queue 3 26 orders in queue 3 38 orders in queue 3 27 orders in queue 2 20 orders in queue 2 56 orders in queue 2 23 orders in queue 2 42 orders in queue 1 30 orders in queue 1 52 orders in queue 1 35 orders in queue 1 29 orders in queue 1 72 orders in queue 1 41 orders in queue 1 45 orders in queue 1 46 orders in queue 1 39 orders in queue 1 54 orders in queue 1 164 orders in queue 1 43 orders in queue 1 44 orders in queue 1 82 orders in queue 1 76 orders in queue 1 215 orders in queue 1 63 orders in queue 1 34 orders in queue 1 161 orders in queue 1 32 orders in queue 1
for null values we assume that they have 0 order in queue¶
In [ ]:
df['Order in Queue'] = df['Order in Queue'].str.extract('(\d+)').fillna(0).astype(int)
print_unique_elements(df, ['Order in Queue'])
Order in Queue: 0 3761 1 822 2 443 3 249 4 159 5 109 6 85 7 50 8 44 11 31 10 29 9 20 12 15 13 11 16 9 14 7 18 7 19 6 15 6 22 6 24 6 31 3 17 3 25 3 21 3 26 3 38 3 27 2 20 2 56 2 23 2 42 1 30 1 52 1 35 1 29 1 72 1 41 1 45 1 46 1 39 1 54 1 164 1 43 1 44 1 82 1 76 1 215 1 63 1 34 1 161 1 32 1
In [ ]:
mapping = {"new seller": 1, "level 1": 2, "level 2": 3, "top rated seller": 4}
df["Seller Level"] = df["Seller Level"].map(mapping)
In [ ]:
numeric_fields = ["Seller Level", "Seller In Same Level", "Basic Price","Standard Price","Premium Price","Basic Delivery","Standard Delivery","Premium Delivery","Basic Revision","Standard Revision","Premium Revision","Rating","Rating Count","Avg Response Time","Last Delivery","Order in Queue"]
df_selected = df[numeric_fields]
corr_matrix = df_selected.corr()
plt.figure(figsize=(12, 8))
sns.heatmap(corr_matrix, annot=True, fmt=".2f", cmap="coolwarm", cbar=True)
plt.title("Correlation Matrix Heatmap")
plt.show()
In [ ]:
config = Settings()
profile = ProfileReport(df, title="training data EDA", config=config)
profile.to_file("fiverr_EDA.html")
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In [ ]:
profile
Out[Â ]:
price fields are not balance and we need to plot without outliers .¶
In [ ]:
def plot_within_3_sigma(df, fields):
for field in fields:
mean = df[field].mean()
sigma = df[field].std()
min_val = mean - 3 * sigma
max_val = mean + 3 * sigma
filtered_data = df[(df[field] >= min_val) & (df[field] <= max_val)]
plt.figure(figsize=(10, 6))
plt.hist(filtered_data[field], bins=30, edgecolor='black')
plt.title(f'Histogram of {field} within mean ± 3 sigma')
plt.xlabel(field)
plt.ylabel('Frequency')
plt.show()
plt.close()
In [ ]:
plot_within_3_sigma(df , numeric_fields)
In [ ]:
df_single_plan = df[df['is_single_plan'] == True]
df_multi_plan = df[df['is_single_plan'] == False]
In [ ]:
df_single_plan.drop(columns=['Standard Price','Premium Price','Standard Delivery','Premium Delivery','Standard Revision','Premium Revision'],axis=1,inplace=True)
C:\Users\Lenovo\AppData\Local\Temp\ipykernel_13740\4002396670.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df_single_plan.drop(columns=['Standard Price','Premium Price','Standard Delivery','Premium Delivery','Standard Revision','Premium Revision'],axis=1,inplace=True)
In [ ]:
df_single_plan.rename(columns={'Basic Price': 'Price','Basic Delivery':'Delivery','Basic Revision':'Revision'},inplace=True)
C:\Users\Lenovo\AppData\Local\Temp\ipykernel_13740\3074068187.py:1: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df_single_plan.rename(columns={'Basic Price': 'Price','Basic Delivery':'Delivery','Basic Revision':'Revision'},inplace=True)
In [ ]:
print_unique_elements(df_single_plan, ["Revision"])
Revision: -1 415 1 87 100 71 2 53 3 30 5 12 4 3 8 1 9 1
In [ ]:
df_single_plan.drop(columns=["Revision"],inplace=True)
C:\Users\Lenovo\AppData\Local\Temp\ipykernel_13740\358071252.py:1: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df_single_plan.drop(columns=["Revision"],inplace=True)
In [ ]:
print_unique_elements(df_single_plan,["Category"])
Category: Digital Marketing 158 Data 111 Lifestyle 100 Business 65 Music & Audio 58 Programming & Tech 55 Graphics & Design 40 Writing & Translation 35 Video & Animation 30 Photography 21
In [ ]:
gc.collect()
Out[Â ]:
997435
In [ ]:
single_plan_profile = ProfileReport(df_single_plan, title="single plan EDA", config=config)
single_plan_profile.to_file("fiverr_single_plan_EDA.html")
C:\Users\Lenovo\AppData\Roaming\Python\Python311\site-packages\ydata_profiling\utils\dataframe.py:137: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df.rename(columns={"index": "df_index"}, inplace=True)
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In [ ]:
gc.collect()
multy_plan_profile = ProfileReport(df_multi_plan, title="single plan EDA", config=config)
multy_plan_profile.to_file("multy_plan_EDA.html")
C:\Users\Lenovo\AppData\Roaming\Python\Python311\site-packages\ydata_profiling\utils\dataframe.py:137: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
df.rename(columns={"index": "df_index"}, inplace=True)
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In [ ]:
single_counts = df_single_plan.count()
multi_counts = df_multi_plan.count()
labels = ["Single Plan", "Multi Plan"]
plt.bar(labels[0], single_counts, color="blue", label="Single Plan")
plt.bar(labels[1], multi_counts, color="green", label="Multi Plan")
plt.title("Proportion of Single Plan vs Multi Plan")
plt.ylabel("Proportion")
plt.show()
In [ ]:
plot_within_3_sigma(df_multi_plan , numeric_fields)
In [ ]:
plot_within_3_sigma(df_single_plan,['Seller Level','Seller In Same Level','Price','Delivery','Rating','Rating Count','Avg Response Time','Last Delivery','Order in Queue'])
In [ ]:
for level in df_multi_plan['Seller Level'].unique():
print(f"Shapiro-Wilk Test for level {level}:", stats.shapiro(df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == level]))
stats.probplot(df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == level], plot=plt)
plt.title(f"Q-Q plot for level {level}")
plt.show()
Shapiro-Wilk Test for level 2: ShapiroResult(statistic=0.2886931300163269, pvalue=0.0)
Shapiro-Wilk Test for level 3: ShapiroResult(statistic=0.3321465253829956, pvalue=0.0)
Shapiro-Wilk Test for level 1: ShapiroResult(statistic=0.21009880304336548, pvalue=0.0)
Shapiro-Wilk Test for level 4: ShapiroResult(statistic=0.29845017194747925, pvalue=0.0)
we are not allowed to use anova here so we use Kruskal-Wallis test instead .
In [ ]:
def plot_average_prices(df, group_by_columns, price_columns, height, orientation = 'horizontal',sort = True , original_order=None):
fig, axs = plt.subplots(1, len(price_columns), figsize=(15, height))
for i, price_column in enumerate(price_columns):
average_prices = df.groupby(group_by_columns)[price_column].mean()
if sort:
average_prices = average_prices.sort_values(ascending=True)
if not sort and original_order :
average_prices = average_prices.reindex(original_order)
if orientation == 'horizontal':
average_prices.plot(kind='barh', color='skyblue', ax=axs[i])
axs[i].set_xlabel(price_column)
else:
average_prices.plot(kind='bar', color='skyblue', ax=axs[i])
axs[i].set_ylabel(price_column)
axs[i].set_title(f'Average {price_column} for Each {group_by_columns}')
axs[i].grid(axis='x' if orientation == 'horizontal' else 'y')
plt.tight_layout()
plt.show()
In [ ]:
H, pvalue = stats.kruskal(df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Basic Price'][df_multi_plan['Seller Level'] == 4])
print('H-statistic:', H)
print('P-value:', pvalue)
H-statistic: 401.05949076441806 P-value: 1.3051530131546888e-86
based on Kruskal-Wallis test result we can say that there is a meaningfull relation between the basic price and seller level .
In [ ]:
H, pvalue = stats.kruskal(df_multi_plan['Standard Price'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Standard Price'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Standard Price'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Standard Price'][df_multi_plan['Seller Level'] == 4])
print('H-statistic:', H)
print('P-value:', pvalue)
H-statistic: 329.41542370806235 P-value: 4.27045420662165e-71
based on Kruskal-Wallis test result we can say that there is a meaningfull relation between the standard price and seller level .
In [ ]:
H, pvalue = stats.kruskal(df_multi_plan['Premium Price'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Premium Price'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Premium Price'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Premium Price'][df_multi_plan['Seller Level'] == 4])
print('H-statistic:', H)
print('P-value:', pvalue)
H-statistic: 272.7436345345654 P-value: 7.868162097551349e-59
based on Kruskal-Wallis test result we can say that there is a meaningfull relation between the premium price and seller level .
In [ ]:
plot_average_prices(df_multi_plan, ["Seller Level"], ["Basic Price","Standard Price","Premium Price"], 10,'vertical',False)
In [ ]:
for level in df_multi_plan['Seller Level'].unique():
print(f"Shapiro-Wilk Test for level {level}:", stats.shapiro(df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == level]))
stats.probplot(df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == level], plot=plt)
plt.title(f"Q-Q plot for level {level}")
plt.show()
Shapiro-Wilk Test for level 2: ShapiroResult(statistic=0.6463815569877625, pvalue=0.0)
Shapiro-Wilk Test for level 3: ShapiroResult(statistic=0.5890364050865173, pvalue=0.0)
Shapiro-Wilk Test for level 1: ShapiroResult(statistic=0.5094095468521118, pvalue=0.0)
Shapiro-Wilk Test for level 4: ShapiroResult(statistic=0.6410760879516602, pvalue=1.8357009882655104e-43)
still cant use anova
In [ ]:
basic_H, basic_pvalue = stats.kruskal(df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Basic Delivery'][df_multi_plan['Seller Level'] == 4])
standard_H, standard_pvalue = stats.kruskal(df_multi_plan['Standard Delivery'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Standard Delivery'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Standard Delivery'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Standard Delivery'][df_multi_plan['Seller Level'] == 4])
premium_H, premium_pvalue = stats.kruskal(df_multi_plan['Premium Delivery'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Premium Delivery'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Premium Delivery'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Premium Delivery'][df_multi_plan['Seller Level'] == 4])
print('basic H-statistic:', basic_H)
print('basic P-value:', basic_pvalue)
print('standard H-statistic:', standard_H)
print('standard P-value:', standard_pvalue)
print('premium H-statistic:', premium_H)
print('premium P-value:', premium_pvalue)
basic H-statistic: 306.11297098017025 basic P-value: 4.7283074898406087e-66 standard H-statistic: 187.023382137299 standard P-value: 2.68275113121452e-40 premium H-statistic: 104.02355272123009 premium P-value: 2.119311520067058e-22
based on Kruskal-Wallis test results we can say that there is a meaningfull relation between the all deliveries and seller level .
In [ ]:
plot_average_prices(df_multi_plan, ["Seller Level"], ["Basic Delivery","Standard Delivery","Premium Delivery"], 10,'vertical',False)
In [ ]:
for level in df_multi_plan['Seller Level'].unique():
print(f"Shapiro-Wilk Test for level {level}:", stats.shapiro(df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == level]))
stats.probplot(df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == level], plot=plt)
plt.title(f"Q-Q plot for level {level}")
plt.show()
Shapiro-Wilk Test for level 2: ShapiroResult(statistic=0.4048423171043396, pvalue=0.0)
Shapiro-Wilk Test for level 3: ShapiroResult(statistic=0.2707735300064087, pvalue=0.0)
Shapiro-Wilk Test for level 1: ShapiroResult(statistic=0.31081730127334595, pvalue=0.0)
Shapiro-Wilk Test for level 4: ShapiroResult(statistic=0.5352907180786133, pvalue=0.0)
In [ ]:
H, pvalue = stats.kruskal(df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == 1],
df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == 2],
df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == 3],
df_multi_plan['Rating Count'][df_multi_plan['Seller Level'] == 4])
print('H-statistic:', H)
print('P-value:', pvalue)
H-statistic: 1017.9958676510408 P-value: 2.2451486399795364e-220
so there is also a meaningful relationship between Rating Count and seller level.
In [ ]:
average_counts = df_multi_plan.groupby("Seller Level")["Rating Count"].mean()
plt.figure(figsize=(10, 6))
average_counts.plot(kind="bar", color="skyblue")
plt.xlabel("Seller Level")
plt.ylabel("Average Rating Count")
plt.title("Average Rating Count for Each Seller Level")
plt.grid(axis="y")
plt.tight_layout()
plt.show()
In [ ]:
W, p = stats.shapiro(df['Avg Response Time'])
print(f'Shapiro-Wilk test statistic: {W}, p-value: {p}')
W, p = stats.shapiro(df['Rating Count'])
print(f'Shapiro-Wilk test statistic: {W}, p-value: {p}')
Shapiro-Wilk test statistic: nan, p-value: 1.0 Shapiro-Wilk test statistic: 0.3117879033088684, p-value: 0.0
c:\Python311\Lib\site-packages\scipy\stats\_morestats.py:1882: UserWarning: p-value may not be accurate for N > 5000.
warnings.warn("p-value may not be accurate for N > 5000.")
In [ ]:
stats.probplot(df['Avg Response Time'], plot=plt)
plt.show()
stats.probplot(df['Rating Count'], plot=plt)
plt.show()
In [ ]:
df_copy = df.copy()
df_copy.dropna(subset=['Avg Response Time', 'Rating Count'], inplace=True)
x = df_copy['Avg Response Time']
y = df_copy['Rating Count']
pearson_coef, p_value = stats.pearsonr(x, y)
print("Pearson's correlation coefficient:", pearson_coef)
Pearson's correlation coefficient: -0.03211739739230705
In [ ]:
stats.probplot(df['Order in Queue'], plot=plt)
plt.show()
In [ ]:
df_copy = df.copy()
df_copy.dropna(subset=['Avg Response Time', 'Order in Queue'], inplace=True)
x = df_copy['Avg Response Time']
y = df_copy['Order in Queue']
spearman_coef, p_value = stats.spearmanr(x, y)
print("Spearman's correlation coefficient:", spearman_coef)
Spearman's correlation coefficient: -0.053936931694605186
In [ ]:
stats.probplot(df['Rating'], plot=plt)
plt.show()
In [ ]:
df_copy = df.copy()
df_copy.dropna(subset=['Avg Response Time', 'Rating'], inplace=True)
x = df_copy['Avg Response Time']
y = df_copy['Rating']
spearman_coef, p_value = stats.spearmanr(x, y)
print("Spearman's correlation coefficient:", spearman_coef)
Spearman's correlation coefficient: 0.09825559680015179
In [ ]:
plot_average_prices(df_multi_plan, "Category", ["Basic Price", "Standard Price", "Premium Price"],5)
In [ ]:
plot_average_prices(df_multi_plan, ["Category","Field"], ["Basic Price", "Standard Price", "Premium Price"],10)
In [ ]:
def calculate_outlier_percentage(series):
q1 = series.quantile(0.25)
q3 = series.quantile(0.75)
iqr = q3 - q1
lower_bound = q1 - 1.5 * iqr
upper_bound = q3 + 1.5 * iqr
outliers_count = ((series < lower_bound) | (series > upper_bound)).sum()
total_count = len(series)
outliers_percentage = (outliers_count / total_count) * 100
return outliers_percentage
In [ ]:
def plot_boxplot(df, x, y):
plt.figure(figsize=(15, 6))
sns.boxplot(y=y, x=x, data=df, orient="h")
plt.title(f"{x} By {y} Box plot")
plt.ylabel(y)
plt.xlabel(x)
plt.show()
outliers_percentage = df.groupby(y)[x].apply(calculate_outlier_percentage)
return outliers_percentage
In [ ]:
print(plot_boxplot(df_multi_plan,"Basic Price","Category"))
print(plot_boxplot(df_multi_plan,"Standard Price","Category"))
print(plot_boxplot(df_multi_plan,"Premium Price","Category"))
Category Business 6.880734 Data 12.641509 Digital Marketing 7.344633 Graphics & Design 11.485149 Lifestyle 7.344633 Music & Audio 5.769231 Photography 7.228916 Programming & Tech 13.643178 Video & Animation 11.157025 Writing & Translation 9.883721 Name: Basic Price, dtype: float64
Category Business 10.091743 Data 11.698113 Digital Marketing 8.757062 Graphics & Design 13.861386 Lifestyle 8.662900 Music & Audio 6.213018 Photography 8.132530 Programming & Tech 13.943028 Video & Animation 11.983471 Writing & Translation 10.465116 Name: Standard Price, dtype: float64
Category Business 12.844037 Data 10.943396 Digital Marketing 7.909605 Graphics & Design 13.267327 Lifestyle 7.344633 Music & Audio 6.656805 Photography 9.638554 Programming & Tech 15.292354 Video & Animation 11.983471 Writing & Translation 9.302326 Name: Premium Price, dtype: float64
In [ ]:
def cat_plot(f1, f2, df, target):
plt.figure(figsize=(15, 6))
sns.catplot(x=f1, y=target, hue=f2, data=df, kind="bar", height=6, aspect=2)
plt.title(f"{target} Distribution by {f1} and {f2}")
plt.xticks(rotation=45)
plt.show()
In [ ]:
cat_plot('Seller Level', 'Category', df_multi_plan, 'Basic Price')
c:\Python311\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
<Figure size 1500x600 with 0 Axes>
In [ ]:
cat_plot("Seller Level", "Category", df_multi_plan, "Standard Price")
c:\Python311\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
<Figure size 1500x600 with 0 Axes>
In [ ]:
cat_plot("Seller Level", "Category", df_multi_plan, "Premium Price")
c:\Python311\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
<Figure size 1500x600 with 0 Axes>
In [ ]:
cat_plot("Category", "Seller Level", df_multi_plan, "Premium Price")
c:\Python311\Lib\site-packages\seaborn\axisgrid.py:118: UserWarning: The figure layout has changed to tight self._figure.tight_layout(*args, **kwargs)
<Figure size 1500x600 with 0 Axes>
In [ ]:
import math
def cat_plot_by_category(df, field_col, target_col, plots_per_row=3):
categories = df["Category"].unique()
num_categories = len(categories)
num_rows = math.ceil(num_categories / plots_per_row)
fig, axes = plt.subplots(num_rows, plots_per_row, figsize=(8 * plots_per_row, 6 * num_rows))
axes = axes.flatten()
for i, category in enumerate(categories):
ax = axes[i]
cat_df = df[df["Category"] == category]
sns.barplot(
x=field_col, y=target_col, hue=field_col, data=cat_df, ax=ax, dodge=False
)
ax.set_title(f"{target_col} Distribution for {category} and Field")
ax.set_ylabel(target_col)
ax.set_xlabel("")
ax.set_xticklabels([])
ax.legend(title=field_col)
for j in range(i + 1, len(axes)):
fig.delaxes(axes[j])
plt.tight_layout()
plt.show()
In [ ]:
cat_plot_by_category(df_multi_plan, "Field", "Basic Price")
cat_plot_by_category(df_multi_plan, "Field", "Standard Price")
cat_plot_by_category(df_multi_plan, "Field", "Premium Price")
